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ABSTRACTSince each frequency senses distinct features of forest structure, it is attractive to understand whether complementarity of multi-frequency can improve the retrieval of forest above ground biomass (AGB). In this study, 15 combinations of X, C, L, and P band SAR observations were applied in the forest AGB estimation through multivariate linear stepwise regression (MLSR), random forest (RF), and deep learning algorithm based on Keras and TensorFlow algorithms to fully explore their potential and capability for forest AGB retrieval. 21 SAR observations were derived from each frequency and worked as SAR observations for forest AGB estimation. According to the retrieval results using three inversion algorithms and 15 SAR observation combinations, MLSR algorithm with combined L and P band SAR observations performed best with R2 = 0.67 and RMSE = 14.51 Mg/ha. Next was RF algorithm with combination of L and P band, the R2 and RMSE are 0.64 and15.20 Mg/ha, respectively. L and P band are more suitable frequencies for estimating forest AGB, only limited improvements can be achieved by combing them with X and C band. Combination of L and P band can obtain comparable AGB estimation accuracy with using combinations from tri- or quad-frequency SAR observations.

The penetration capability of electromagnetic wave signal into forest increases with increasing wavelength. SAR data at each frequency senses different components of forest structure. Therefore the biomass distributed at various tree components could be estimated using different radar frequencies. Additionally, the synthesis of multiple SAR frequencies could improve the accuracy in retrieving forest above-ground biomass (AGB). Taking advantage of available X-, C-, L-, and P-band quad-polarimetric SAR images of airborne or spaceborne for the test site located at Genhe national forest scientific field station, we used a Genetic Algorithm and Support Vector Regression optimization algorithm (GA-SVR) to explore the sensitivity of polarimetric observations at various frequencies to forest AGB and effectiveness of AGB retrievals using single-frequency, dual-frequency, triple-frequency, and quad-frequency SAR observation combinations. We found that: (i) Most of the polarimetric observations are sensitive to forest AGB, (ii) GA-SVR performed well in forest AGB retrieval using the single frequency SAR observations or combinations of multi-frequency observations; the highest Acc. value for single-frequency-retrieved results is 75.13% acquired at P-band, with multi-frequency, the highest Acc. values is 77.34% acquired by combining C- and P-band. (iii) For forest AGB retrievals, the single-frequency P-band accuracy is comparable to the combined C- and P-band retrieval accuracy, indicating that the long-wavelength single-frequency P-band is sufficient for an accurate forest AGB retrieval. The findings reconfirmed potential of P-band for forest AGB retrievals, they also demonstrated that the optimal combination of multi-frequency SAR for AGB retrievals is by using a short-wavelength (X/C-) and a long-wavelength (L/P-).

As urbanization accelerates, characteristics of urban spatial expansion play a significant role in the future utilization of land resources, the protection of the ecological environment, and the coordinated development of population and land. In this study, Yunnan and Guizhou provinces were selected as the study area, and the 2013–2021 National Polar-Orbiting Partnership’s Visible Infrared Imaging Radiometer Suite (NPP-VIIRS) nighttime light (NTL) data were utilized for spatial and temporal change analysis of urban built-up areas. Firstly, the built-up areas in Yunnan and Guizhou provinces were extracted through ENUI (Enhanced Nighttime Lighting Urban Index) indices, and then the urban expansion speed and urban center of gravity migration were constructed and used to explore and analyze the spatial and temporal change and expansion characteristics of built-up areas in Yunnan and Guizhou provinces. The results showed the following. (1) Due to the complementarity between data types, such as NTL, EVI, NDBI, and NDWI, ENUI has better performance in expressing urban characteristics. (2) Influenced by national and local policies, such as “One Belt, One Road”, transportation infrastructure construction, geographic location, the historical background, and other factors, the urban expansion rate of Yunnan and Guizhou provinces in general showed a continuous advancement from 2013 to 2021, and there were three years in which the expansion rate was positive. (3) The center of gravity migration distance of most cities in Guizhou Province shows a trend of increasing and then decreasing, while the center of gravity migration distance in Yunnan Province shows a trend of continuous decrease in general. From the perspective of migration direction, Guizhou Province has the largest number of migrations to the northeast, while Yunnan Province has the largest number of migrations to the southeast. (4) Influenced by policy, economy, population, geography, and other factors, urban compactness in Yunnan and Guizhou provinces continued to grow from 2013 to 2021. The results of this study can help us better understand urbanization in western China, reveal the urban expansion patterns and spatial characteristics of Yunnan and Guizhou provinces, and provide valuable references for development planning and policymaking in Yunnan and Guizhou provinces.

Forest height, as a measure of the quantity and quality of forest resources, plays a significant role in the study of the ecological functions performed by forests. Although the polarimetric synthetic aperture radar interferometry (PolInSAR) technique has evolved as a potent method for forest height inversion, uncertainties still exist in the process of estimating forest height, and the uncertainties in predicted forest height directly lead into the uncertainty of terrestrial carbon stock calculation results. In this study, we took the Random Volume over Ground (RVoG) model as likelihood function and constructed a hierarchical Bayesian framework to calculate and reduce the uncertainty of forest height inversion using L / P band PolInSAR airborne data via RVoG model. Uncertainties resulted from five canopy types and three forest densities were analyzed, respectively. The results showed that among the five different canopy types, L band has the highest prediction accuracy in pure coniferous canopy with Acc. = 0.90. The uncertainty is extremely low for pure forest, with the ratio of uncertainty values of 0.09 for L band and 0.15 for the P band in pure coniferous canopy, and uncertainty values of 0.16 for L band and 0.11 for P band in pure broadleaf canopy, respectively. Furthermore, when the forest density is between 300 and 600 stems/ha, the ratio of uncertainties for the L band is 0.27, whereas the P band is 0.24. As forest density increases, the uncertainty in forest height estimates decreases for both bands. The changes in canopy types and forest density affect forest height estimation uncertainties obviously, the effects are different at each frequency. The forest height inversion accuracy of the L band in pure coniferous canopy surpasses that in other canopy types, with the lowest uncertainty. P band performed well in broadleaf canopy forest height inversion. The inversion uncertainties at both frequencies decrease with increase of forest densities.

Exploring NDVI variation and what drives it on the Qinghai–Tibet Plateau can strategically inform environmental protection efforts in light of global climate change. For this analysis, we obtained MODIS NDVI data collected during the vegetative growing season, vegetation types for the region, and meteorological data for the same period from 2001 to 2020. We performed Theil–Sen trend analysis, Mann–Kendall significance testing, spatial autocorrelation analysis, and Hurst index calculation to review the spatiotemporal changes in NDVI characteristics on the plateau for various vegetation types. We used the correlation coefficients from these analyses to investigate how the NDVI responds to temperature and precipitation. We found the following: (1) Overall, the Qinghai–Tibet Plateau NDVI increased throughout the multi-year growing season, with a much larger area of improvement (65.68%) than of degradation (8.83%). (2) The four main vegetation types were all characterized by improvement, with meadows (72.13%) comprising the largest portion of the improved area and shrubs (18.17%) comprising the largest portion of the degraded area. (3) The spatial distribution of the NDVI had a strong positive correlation and clustering effect and was stable overall. The local clustering patterns were primarily low–low and high–high clustering. (4) The Hurst index had an average value of 0.46, indicating that the sustainability of vegetation is poor; that is, the trend of vegetation change in the growing season in a large part of the Qinghai–Tibet Plateau in the future is opposite to that in the past. (5) The plateau NDVI correlated positively with air temperature and precipitation. However, the correlations varied geographically: air temperature had a wide influence, whereas precipitation mainly influenced meadows and grassland in the northern arid zone. The overall temperature-driven effect was stronger than that of precipitation. This finding is consistent with the current research conclusion that global warming and humidification promote vegetation growth in high-altitude areas and further emphasizes the uniqueness of the Qinghai–Tibet Plateau as a climate-change-sensitive area. This study also offers a technical foundation for understanding how climate change impacts high-altitude ecosystems, as well as for formulating ecological protection strategies for the plateau.

Forest aboveground biomass (AGB) and its biomass components are key indicators for assessing forest ecosystem health, productivity, and carbon stocks. Light Detection and Ranging (LiDAR) technology has great advantages in acquiring the vertical structure of forests and the spatial distribution characteristics of vegetation. In this study, the 56 features extracted from airborne LiDAR point cloud data were used to estimate forest total and component AGB. Variable importance–in–projection values calculated through a partial least squares regression algorithm were utilized for LiDAR-derived feature ranking and optimization. Both leave-one-out cross-validation (LOOCV) and cross-validation methods were applied for validation of the estimated results. The results showed that four cumulative height percentiles (AIH30,AIH40, AIH20, and AIH25), two height percentiles (H8 and H6), and four height-related variables (Hmean, Hsqrt, Hmad, and Hcurt) are ranked more frequently in the top 10 sensitive features for total and component forest AGB retrievals. Best performance was acquired by random forest (RF) algorithm, with R2 = 0.75, root mean square error (RMSE) = 22.93 Mg/ha, relative RMSE (rRMSE) = 25.30%, and mean absolute error (MAE) = 19.26 Mg/ha validated by the LOOCV method. For cross-validation results, R2 is 0.67, RMSE is 24.56 Mg/ha, and rRMSE is 25.67%. The performance of support vector regression (SVR) for total AGB estimation is R2 = 0.66, RMSE = 26.75 Mg/ha, rRMSE = 28.62%, and MAE = 22.00 Mg/ha using LOOCV validation and R2 = 0.56, RMSE = 30.88 Mg/ha, and rRMSE = 31.41% by cross-validation. For the component AGB estimation, the accuracy from both RF and SVR algorithms was arranged as stem > bark > branch > leaf. The results confirmed the sensitivity of LiDAR-derived features to forest total and component AGBs. They also demonstrated the worse performance of these features for retrieval of leaf component AGB. RF outperformed SVR for both total and component AGB estimation, the validation difference from LOOCV and cross-validation is less than 5% for both total and component AGB estimated results.

Outdoor recreation plays a pivotal role in improving people’s physical and mental health, serving as a popular form of entertainment and a significant economic contributor. Limited access to these resources not only exacerbates health disparities but also deprives underserved areas of essential benefits like stress relief and community bonding, both of which are crucial for enhancing overall quality of life. This paper provides one of the first detailed analyses of water-based recreation at over 61 000 inland and coastal sites across the United States. We aim to explore disparities in recreational behavior across race, ethnicity, income, and socioeconomic status. Using Advan cellphone data from more than 70 million outdoor trips, representing 215 000 census block groups, we find that communities of color, rural areas, and socioeconomically disadvantaged groups are significantly underrepresented in water-based recreational visits. Despite living similar distances from recreational sites, these groups show notably different patterns in travel distance for water-based recreation. Additionally, we find Native Americans from underserved areas have to travel 3–5 times longer distances than other groups for water-based recreation. Our findings show that the extensive and frequent cellphone mobility data could reveal policy-relevant patterns especially those made by underserved Americans often overlooked in traditional household surveys.

A reliable evaluation of biomass is a vital prerequisite for realizing the international goal of “emission peak and carbon neutrality”. It is critical to estimate the components of forest biomass, for ecosystem management. Additionally, working on components we may solve the saturation problems in AGB estimation using remote sensing features. In our previous works we proposed GA-SVR (Genetic algorithms and support vector regression) algorithm with polarimetric SAR (Synthetic Aperture Rader) to retrieve total forest Above Ground Biomass (AGB) estimation in our previous works, however, the potential of GA-SVR algorithm applied in component AGB estimation especially using combination of multi-frequency polarimetric SAR features deserves further exploration. In this study, we use quad-polarimetric SAR data at C- and L- bands, extracting the backscatter coefficients and polarimetric features derived from four polarization decomposition methods (Yamaguchi 3-component decomposition, Freeman 2-component decomposition, H/A/alpha decomposition, and TSVM decomposition) as the input to the GA-SVR for forest component AGB estimation. The effectiveness of 66 polarimetric features derived from C-, L-band at each test site was evaluated for forest component AGB prediction at two test sites. The outcomes demonstrated that the GA-SVR attained high estimation accuracy according to the values of coefficient of determination R2, root mean square error, relative root mean square error, mean deviation, mean absolute deviation, mean percentage error, and mean absolute percentage error. The highest attained values of them were 0.77, 1.01 Mg/ha, 23.02%, −0.07 Mg/ha, 0.71 Mg/ha, 0.15%, and 18.42%, respectively. The study reconfirmed the robustness of GA-SVR algorithm and effectiveness of polarimetric SAR features extracted from four decomposition methods for forest total and AGB estimation. It also revealed that the capability of combining C- band L-band SAR polarimetric features for improving forest total and component AGB relies on the difference of forest structures.

Synthetic aperture radar (SAR) features have been demonstrated that they have the potentiality to improve forest above ground biomass (AGB) estimation accuracy, especially including polarimetric information. Genetic algorithms (GAs) have been successfully implemented in optimal feature identification, while support vector regression (SVR) has great robustness in parameter estimation. The use of combined GAs and SVR can improve the accuracy of forest AGB estimation through simultaneously identifying the optimal SAR features and selecting the SVR model parameters. In this article, 14 SAR polarimetric features were extracted from C-band and L-band full-polarization SAR images and worked as input SAR features, respectively. C-band data was acquired on GaoFen-3 mission, we also call it GF-3 image. L-band data was ALOS-2 PALSAR-2 data. Both feature subsets from GF-3 and ALOS-2 PALSAR-2 and SVR hyper parameters used in the forest AGB estimation were optimized by a GA processing, where 8 different settings of 3 kinds of parameters, as 512 kind of different combinations were applied for SVR hyper parameters searching field. The results of GA-SVR performance using the two datasets were presented and compared with two traditional methods: the algorithm of GA feature selection companied with default SVR parameters (GA+ default SVR), and the algorithm of GA feature selection companied with grid searching for SVR parameter selection (GA+Grid SVR). The results showed that the proposed GA-SVR algorithm improved the forest AGB estimation accuracy with cross-validation coefficient of 80.21% for GF-3 and 71.41% for ALOS-2 PALSAR-2 data.

Forest structure plays an important role in forest biomass inversion using synthetic aperture radar (SAR) backscatter. Synthetic aperture radar (SAR) sensors with long-wavelength have the potentiality to provide reliable and timely forest biomass inversion for their ability of deep penetration into the forest. L-band SAR backscatter shows useful for forest above-ground biomass (AGB) estimation. However, the way that forest structure mediating the biomass-backscatter affects the improvement of the related biomass estimation accuracy. In this paper, we have investigated L-band SAR backscatter sensitivity to forests with different mean canopy density, mean tree height and mean DBH (diameter at breast height) at the sub-compartment level. The forest species effects on their relationship were also considered in this study. The linear correlation coefficient R, non-linear correlation parameter, Maximal Information Coefficient (MIC), and the determination coefficient R2 from linear function, Logarithmic function and Quadratic function were used in this study to analyze forest structural properties effects on L-band SAR backscatter. The HV channel, which is more sensitive than HH to forest structure parameters, was chosen as the representative of SAR backscatter. 6037 sub-compartment were involved in the analysis. Canopy density showed a great influence on L-band backscatter than mean forest height and DBH. All of the R between canopy density and L-band backscatter were greater than 0.7 during the forest growth cycle. The sensitivity of L-band backscatter to mean forest height depends on forest canopy density. When canopy density was lower than 0.4, R values between mean forest height are smaller than 0.5. In contrast, the values of R were greater than 0.8 if canopy density was higher than 0.4. The sensitivity SAR backscatter to DBH fluctuated with canopy density, but it only showed obvious sensitivity when canopy density equals to 0.6, where both the linear and non-liner correlation values are higher than others. However, their effects on L-bang HV backscatter are affected by forest species, the effects on three forest structural parameters depend on tree species.

Forest biomass change monitoring is essential for climate change. Synthetic aperture radar (SAR) and optimal remote sensing (RS) data are two very helpful data sources for forest biomass monitoring and estimation. During the procedure of biomass estimation using RS technique, optimal features selection and estimation models used are two critical steps. This paper therefore focuses on building an operational and robust method of biomass retrieval using optical and SAR RS data. First, random forest (RF) algorithms are used for reducing time-consuming and decreasing computational burden; then, an iterative procedure was embedded in K-nearest neighbor (KNN) algorithms for the best optimal feature selection and combination; last, the best feature combinations and KNN models were applied for forest biomass estimation. Moreover, forest type effects and RS feature source effects were considered. The results showed that feature combination of two optical images and the SAR image showed highest estimation accuracy by using the proposed algorithm (R2 = 0.70 for Forest-1, R2 = 0.72 for Forest-2, and R2 = 0.71 for Forest-3; RMSE = 16.18 Mg/ha for Forest-1, RMSE =17.66 Mg/ha for Forest-2, and RMSE = 18.67 Mg/ha for Forest-3, where Forest-1 is natural pure forests of Yunnan Pines, Forest-2 is natural mixed coniferous forests, and Forest-3 is the combination of Forest-1 and Forest-2). With the comparative analysis of proposed algorithm and different non-parametric algorithms, traditional nonparametric algorithms performed better in Forest-1, but worse in Forest-2 and Forest-3, while the proposed algorithm performed no obvious difference in three forest types and using five feature groups. The results revealed that the proposed algorithm was robust in biomass estimation, with almost no feature source and forest structure dependent for biomass estimation.

In this study, 27 polarimetric parameters were extracted from Radarsat-2 polarimetric synthetic aperture radar (SAR) at each growth stage of the rape crop. The sensitivity to growth parameters such as stem height, leaf area index (LAI), and biomass were investigated as a function of days after sowing. Based on the sensitivity analysis, five empirical regression models were compared to determine the best model for stem height, LAI, and biomass inversion. Of these five models, quadratic models had higher R2 values than other models in most cases of growth parameter inversions, but when these results were related to physical scattering mechanisms, the inversion results produced overestimation in the performance of some parameters. By contrast, linear and logarithmic models, which had lower R2 values than the quadratic models, had stable performance for growth parameter inversions, particularly in terms of their performance at each growth stage. The best biomass inversion performance was acquired by the volume component of a quadratic model, with an R2 value of 0.854 and root mean square error (RMSE) of 109.93 g m−2. The best LAI inversion was also acquired by a quadratic model, but used the radar vegetation index (Cloude), with an R2 value of 0.8706 and RMSE of 0.56 m2 m−2. Stem height was acquired by scattering angle alpha ( α ) using a logarithmic model, with an R2 of 0.926 value and RMSE of 11.09 cm. The performances of these models were also analysed for biomass estimation at the second growth stage (P2), third growth stage (P3), and fourth growth stage (P4). The results showed that the models built at the P3 stage had better substitutability with the models built during all of the growth stages. From the mapping results, we conclude that a model built at the P3 stage can be used for rape biomass inversion, with 90% of estimation errors being less than 100 g m−2.

Growth parameters like biomass, leaf area index (LAI) and stem height play an import role for crop monitoring and yield prediction. Compact polarimetric (CP) SAR has shown great potential and similar performance to fully-polarimetric (FP) SAR in crop mapping and phenology retrieval, but its potential in growth parameters inversion has not been fully explored. In this paper, a time series of images of CP SAR was simulated from five FP SAR data gathered during the entire growth season of rape. CP response of 27 parameters, relying on Stokes parameters and their child parameters, decomposition parameters and backscattering coefficients, were extracted and investigated as a function of days after sowing (DAS) during the whole rape growth cycle to interpret their sensitivity to each growth parameter. Then, random forest (RF) was chosen as an automatic approach for the growth parameters inversion method, and its results were compared with traditional single-parameter regression models. Most of the CP parameters showed high sensitivity with growth parameters and great potential for growth parameters inversion. Among all of the regression models, the quadratic regression model showed the best performance for all of the growth parameters inversion, the best result for biomass inversion was the third component of the Stokes parameters (g3) with R2 of 0.765 and RMSE of 73.20 g/m2. For LAI and stem height was one of the Stokes child parameters, the circular polarization ratio (Uc), with R2 of 0.857 and 0.923 and RMSE of 0.66 and 18.71 cm, respectively. RF showed the highest accuracy and smallest RMSE for all of three growth parameters inversion; R2 for biomass, LAI and stem height were 0.93, 0.96 and 0.95, respectively; RMSE were 46.24 g/m2, 0.25 and 13.5 cm, respectively. However, there are also some CP parameters, which showed low sensitivity to growth parameters, that had high importance for RF inversion. The results confirmed the potential of CP data and the RF method in growth parameters inversion, but they also confirmed that it was difficult to give a physical interpretation for the RF inversion model.

Bioscaffolds with characteristics of the tumor microenvironment is an important substrate to support the formation of multicellular tumor spheroids (MCTS). Conventional biomaterials promote MCTS formation using tumor extracellular matrix (ECM) components and exogenous growth factors, but lack individualized tumor cell niche construction which may restricts cell biology and drug discovery research. Herein, an ECM mimicked fiber composite dual-network hydrogel (FCDNG) was constructed. The 3D nanofibrous framework was introduced into the hydrogel by a “dispersion-swelling” method and an alginate/platelet-rich plasma (PRP) dual-network hydrogel was constructed by a simple “one-step” activation process. The scaffold can well control the physicochemical conditions that mimic the ECM structure and biomimetic mechanical properties. The fibrin formed by PRP can promote tumor cell proliferation, angiogenesis and cancer stem cell enrichment. Meanwhile, FCDNG has suitable degradation properties to meet the matrix remodeling requirements during the formation of MCTS. Tumor cells cultured in FCDNG had enhanced tumor stemness, proliferation, and resistance to chemotherapeutic agents, and thus providing an effective strategy for clinical drug evaluation.

Abstract Background The use of ionic liquids (ILs) to fractionate lignocelluloses for various bio-based chemicals productions is in the ascendant. On this basis, the protic ILs consisting of triethylammonium hydrogen sulfate ([TEA][HSO4]) possessed great promise due to the low price, low pollution, and high efficiency. In this study, the microwave-assistant [TEA][HSO4] fractionation process was established for corn stover fractionation, so as to facilitate the monomeric sugars production and supported the downstream acetone–butanol–ethanol (ABE) fermentation. Results The assistance of microwave irradiation could obviously shorten the fractionation period of corn stover. Under the optimized condition (190 W for 3 min), high xylan removal (93.17 ± 0.63%) and delignification rate (72.90 ± 0.81%) were realized. The mechanisms for the promotion effect of the microwave to the protic ILs fractionation process were ascribed to the synergistic effect of the IL and microwaves to the depolymerization of lignocellulose through the ionic conduction, which can be clarified by the characterization of the pulps and the isolated lignin specimens. Downstream valorization of the fractionated pulps into ABE productions was also investigated. The [TEA][HSO4] free corn stover hydrolysate was capable of producing 12.58 g L−1 of ABE from overall 38.20 g L−1 of monomeric sugars without detoxification and additional nutrients supplementation. Conclusions The assistance of microwave irradiation could significantly promote the corn stover fractionation by [TEA][HSO4]. Mass balance indicated that 8.1 g of ABE and 16.61 g of technical lignin can be generated from 100 g of raw corn stover based on the novel fractionation strategy.

Abstract Background The effective valorization of lignin and carbohydrates in lignocellulose matrix under the concept of biorefinery is a primary strategy to produce sustainable chemicals and fuels. Based on the reductive catalytic fractionation (RCF), lignin in lignocelluloses can be depolymerized into viscous oils, while the highly delignified pulps with high polysaccharides retention can be transformed into various chemicals. Results A biorefinery paradigm for sequentially valorization of the main components in poplar sawdust was constructed. In this process, the well-defined low-molecular-weight phenols and bioethanol were co-generated by tandem chemo-catalysis in the RCF stage and bio-catalysis in fermentation stage. In the RCF stage, hydrogen transfer reactions were conducted in one-pot process using Raney Ni as catalyst, while the isopropanol (2-PrOH) in the initial liquor was served as a hydrogen donor and the solvent for lignin dissolution. Results indicated the proportion of the 2-PrOH in the initial liquor of RCF influenced the chemical constitution and yield of the lignin oil, which also affected the characteristics of the pulps and the following bioethanol production. A 67.48 ± 0.44% delignification with 20.65 ± 0.31% of monolignols yield were realized when the 2-PrOH:H2O ratio in initial liquor was 7:3 (6.67 wt% of the catalyst loading, 200 °C for 3 h). The RCF pulp had higher carbohydrates retention (57.96 ± 2.78 wt%), which was converted to 21.61 ± 0.62 g/L of bioethanol with a yield of 0.429 ± 0.010 g/g in fermentation using an engineered S. cerevisiae strain. Based on the mass balance analysis, 104.4 g of ethanol and 206.5 g of lignin oil can be produced from 1000 g of the raw poplar sawdust. Conclusions The main chemical components in poplar sawdust can be effectively transformed into lignin oil and bioethanol. The attractive results from the biorefinery process exhibit great promise for the production of valuable biofuels and chemicals from abundant lignocellulosic materials.

AbstractContext Endometrial cancer is a common gynecologic malignancy. Vitexin is an active flavonoid compound with an antitumor function.Objective This study elucidated the role of vitexin in endometrial cancer development and clarified the potential mechanism.Materials and methods The toxicity of vitexin (0–80 μM) treatment for 24 h on HEC-1B and Ishikawa cells was tested utilizing the CCK-8 assay. Endometrial cancer cells were divided into vitexin 0, 5, 10, and 20 μM groups. Cell proliferation, angiogenesis and stemness in vitro after treatment with vitexin (0, 5, 10, 20 μM) for 24 h were evaluated using the EdU staining assay, tube formation assay and sphere formation assay, respectively. Twelve BALB/c mice were grouped into control and vitexin (80 mg/kg) groups to monitor tumour growth for 30 days.Results Vitexin suppressed cell viability of HEC-1B (IC50 = 9.89 μM) and Ishikawa (IC50 = 12.35 μM) cells. The proliferation (55.3% and 80% for HEC-1B; 44.7% and 75% for Ishikawa), angiogenesis (54.3% and 78.4% for HEC-1B; 47.1% and 68.2% for Ishikawa) and stemness capacity (57.2% and 87.3% for HEC-1B; 53.4% and 78.4% for Ishikawa) of endometrial cancer cells were inhibited by 10 and 20 μM vitexin. Furthermore, the inhibitory effects of vitexin on endometrial cancer were reversed by PI3K/AKT agonist 740Y-P (20 μM). Moreover, the xenograft tumour experiment lasting for 30 days proved that vitexin (80 mg/kg) blocked tumour growth of endometrial cancer in vivo.Discussion and conclusions Vitexin has therapeutic potential on endometrial cancer, which supports further clinical trials.