Heavy metal stress in crops is a worldwide problem that requires accurate and timely monitoring. This study aimed to improve the accuracy of monitoring heavy metal stress levels in rice by using multiple Sentinel-2 images. The selected study areas are in Zhuzhou City, Hunan Province, China. Six Sentinel-2 images were acquired in 2017, and heavy metal concentrations in soil were measured. A novel vegetation index called heavy metal stress sensitive index (HMSSI) was proposed. HMSSI is the ratio between two red-edge spectral indices, namely the red-edge chlorophyll index (CI_red-edge) and the plant senescence reflectance index (PSRI). To demonstrate the capability of HMSSI, the performances of CI_red-edge and PSRI in discriminating heavy metal stress levels were compared with that of HMSSI at different growth stages. Random forest (RF) was used to establish a multitemporal monitoring model to detect heavy metal stress levels in rice based on HMSSI at different growth stages. Results show that HMSSI is more sensitive to heavy metal stress than CI_red-edge and PSRI at different growth stages. The performance of a multitemporal monitoring model combining the whole growth stage images was better than any other single growth stage in distinguishing heavy metal stress levels. Therefore, HMSSI can be regarded as an indicator for monitoring heavy metal stress levels with a multitemporal monitoring model.

Precise simulation of crop growth is crucial to yield estimation, agricultural field management, and climate change. Although assimilation of crop model and remote sensing data has been applied in crop growth simulation, few studies have considered optimizing the crop model with respect to phenology. In this study, we assimilated phenological information obtained from Moderate Resolution Imaging Spectroradiometer (MODIS) time series data into the World Food Study (WOFOST) model to improve the accuracy of rice growth simulation at the regional scale. The particle swarm optimization (PSO) algorithm was implemented to optimize the initial phenology development stage (IDVS) and transplanting date (TD) in the WOFOST model by minimizing the difference between simulated and observed phenology, including heading and maturity date. Assimilating phenology improved the accuracy of the rice growth simulation, with correlation coefficients (R) equal to 0.793, 0822, and 0.813 at three fieldwork dates. The performance of the proposed strategy is comparable with that of the enhanced vegetation index (EVI) time series assimilation strategy, with less computation time. Additionally, the result confirms that the proposed strategy could be applied with different spatial resolution images and the difference of simulated LAI_mean is less than 0.35 in three experimental areas. This study offers a novel assimilation strategy with regard to the phenology development process, which is efficient and scalable for crop growth simulation.

Timely assessment of crop growth conditions under heavy metal pollution is of great significance for agricultural decision-making and estimation of crop productivity. The object of this study is to assess the effects of heavy metal stress on physiological functions of rice through the spatial-temporal analysis of the fraction of absorbed photosynthetically active radiation (FAPAR). The calculation of daily FAPAR is conducted based on a coupled model consisting of the leaf-canopy radiative transfer model and World Food Study Model (WOFOST). These two models are connected by leaf area index (LAI) and a fraction of diffused incoming solar radiation (SKYL) in the rice growth period. The input parameters of the coupled model are obtained from measured data and GF-1 images. Meanwhile, in order to improve accuracy of FAPAR, the crop growth model is optimized by data assimilation. The validation result shows that the correlation between the simulated FAPAR and the measured data is strong in the rice growth period, with the correlation coefficients being above 7.5 for two areas. The discrepancy of FAPAR between two areas of different stress levels is visualized by spatial-temporal analysis. FAPAR discrepancy starts to appear in the jointing-booting period and experiences a gradual rise, reaching its maximum in the heading-flowering stage. This study suggests that the coupled model, consisting of the leaf-canopy radiative transfer model and the WOFOST model, is able to accurately simulate daily FAPAR during crop growth period and FAPAR can be used as a potential indicator to reflect the impact of heavy metal stress on crop growth.

Predicting Chlorophyll-a (Chla) is essential to support the marine environment changes, marine ecosystem health, and provide early warning of algae blooms. The development of learning-based methods has facilitated Chla prediction research. Still, most of the current methods can only predict short-term Chla changes in small areas, which is limited by the ability of the model to exploit spatiotemporal dependencies. Thus, this paper proposes a spatiotemporal fusion Transformer prediction model (STF_Transformer) to predict relatively long-term Chla changes (15 days ahead). This model utilizes temporal and spatial Transformer modules to extract the temporal and spatial correlations of the input spatiotemporal sequences, which are then fused to predict the 15-day Chla. The experimental results show that the proposed model has the optimal performance compared to the existing methods (e.g. CNN, LSTM, and ConvLSTM), with Root Mean Squared Error (RMSE), Mean Absolute Error (MAE), and Mean Absolute Percentage Error (MAPE) less than 0.61 mg/m ³ , 0.315 mg/m ³ and 22.5%, respectively, for 15 days Chla prediction in a large area (including Bohai, Yellow, and East China Sea). In addition, the temporal and spatial prediction results of the proposed model show that the predicted Chla has consistent temporal and spatial patterns with the observed Chla. This study indicates that the proposed STF_Transformer model can provide a highly accurate prediction of Chla over a large area in the relatively long term (15 days), providing data and technical support for marine ecosystem-related applications.

Urban greening is increasingly promoted as a means to achieve sustainable and inclusive development. However, it remains unclear whether the expansion of green infrastructure reduces spatial and socioeconomic inequalities or reinforces them. This study examines the long-term dynamics of green space exposure in 287 Chinese cities from 2000 to 2020 using geospatial and statistical data. While median green coverage and exposure increased by 126% and 135%, spatial disparities also grew. Eastern cities consistently showed higher levels of exposure, and national-level improvements did not translate into proportional narrowing of interregional gaps. Granger causality tests indicate a bidirectional relationship between economic growth and green exposure at the national level. This relationship varies across regions. Cities in the east, center, and northeast exhibit strong mutual effects, while other regions show weaker or unidirectional linkages. In North China, economic development and green exposure are entirely disconnected. These differences have led to divergent patterns in exposure equity. Despite a 44% national decline in the Gini index, disparities persisted, most notably in northeastern China. The results suggest that area-based greening targets are insufficient to address inequality. Planning strategies that consider population exposure and regional conditions are needed to improve the inclusiveness of urban greening. This study offers evidence for designing more equitable greening approaches in rapidly urbanizing areas.

Despite substantial reductions in ambient particulate matter with a diameter of 2.5 µm or less (PM_2.5) concentrations across China since the Air Pollution Prevention Plan began in 2013, the associated health and economic burdens remain unevenly distributed, reinforcing structural inequalities. Here, we quantify PM_2.5-attributable mortality and economic losses from 2005 to 2020 using an age- and region-specific value of statistical life, and construct a pollution-adjusted income index to assess real income erosion. Mortality rates declined by 7.51%, but economic losses surged more than fivefold to 1.931 trillion Chinese Yuan in 2020, driven by rising life valuation and persistent exposure. National income improved, yet gains were uneven; less-developed regions experienced limited net progress as environmental costs eroded constrained incomes. Including these costs reveals higher inequality in real income, underscoring the need to integrate environmental externalities into welfare assessments and prioritize mitigation in vulnerable regions for just and inclusive sustainability.