With the widespread application of multi-view data across various domains, multi-view unsupervised feature selection (MUFS) has achieved remarkable progress in both feature selection (FS) and missing-view completion. However, existing MUFS methods typically rely on centralized servers, which not only fail to meet privacy requirements in distributed settings but also suffer from suboptimal FS quality and poor convergence. To overcome these challenges, we propose a novel federated incomplete MUFS method (Fed-IMUFS), which integrates a fractional Sparsity-Guided Whale Optimization Algorithm (SGWOA) and Tensor Alternating Learning (TAL). Within this federated learning framework, each client performs local optimization in two stages: in the first stage, SGWOA introduces an $L_{2,1}$ proximal projection to enforce row-sparsity in the FS weight matrix, while fractional-order dynamics and fractal-inspired elite kernel injection mechanisms enhance global search ability, yielding a discriminative and stable weight matrix; in the second stage, based on the obtained weight matrix, an alternating optimization framework with tensor decomposition is employed to iteratively complete missing views while simultaneously optimizing low-dimensional representations to preserve cross-view consistency, with the objective function gradually minimized until convergence. During federated training, the server employs an aggregation and distribution strategy driven by normalized mutual information, where clients upload only their local weight matrices and quality indicators, and the server adaptively fuses them into a global FS matrix before distributing it back to clients. This process achieves consistent FS across clients while safeguarding data privacy. Comprehensive evaluations on CEC2022 and several incomplete multi-view datasets confirm that Fed-IMUFS outperforms state-of-the-art methods, delivering stronger global optimization capability, higher-quality feature selection, faster convergence, and more effective handling of missing views.

The balance between gross primary productivity (GPP) and ecosystem respiration (ER) defines an ecosystem’s carbon sink-source status. Under global warming, hydrothermal conditions critically shape carbon fluxes, yet their differential impacts on GPP and ER remain insufficiently understood, especially across biomes. Elucidating these differences is essential for reducing uncertainties in terrestrial carbon cycle projections under ongoing climate change. Here, based on flux observations from global terrestrial sites with a focus on forest ecosystems, we selected mean annual temperature (MAT), latent heat flux (LH), vapor pressure deficit (VPD), soil water content (SWC), and annual precipitation as representative indicators of hydrothermal conditions, and employed mixed-effects models to examine how these key environmental drivers influence GPP and ER. After analyzing the fixed effects, LH and MAT promoted GPP more strongly than ER (slope = 0.5 > 0.253, slope = 0.595 > 0.392, respectively), whereas VPD suppressed GPP more than ER (slope = −0.658 < −0.499), yet accounted for a greater proportion of variance in ER than in GPP (R² = 0.14 > 0.07). Although SWC had a significant (p < 0.001) positive effect on GPP, the effect size was minimal, and its impact on ER was insignificant. R² decomposition showed that marginal R² values were similar for the GPP and ER models (0.43 and 0.44), whereas the GPP model exhibited a substantially higher conditional R² (0.82 vs. 0.63), indicating that MAT exerted a stronger influence on GPP than on ER across ecosystem types. The combined analysis of fixed and random effects indicated that MAT affected GPP more variably than ER across ecosystem types, with the strongest responses in mixed forests and savannas, intermediate responses in evergreen needleleaf forests, and the weakest responses in evergreen broadleaf forests. Overall, this study advances our understanding of how environmental factors differently influence GPP and ER, and incorporating these differences can improve predictions of forest carbon fluxes and climate-carbon feedbacks.

Climate change and growing populations are major challenges for food security. Understanding single-season rice (SSR) growth patterns and how much area changes over time is essential for sustaining rice distribution patterns and ensuring food security. This study utilized ground trothing data with the remote sensing (RS) technique for estimation of the SSR pattern in Jiangsu Province. A total of 1700 rice and 470 non-rice points were collected during the field visit in April–September 2023 across Jiangsu Province. The current study employed advanced machine learning (ML) and the random forest (RF) model using Google Earth Engine (GEE). This study evaluates the SSR cropping area, including the Normalized Difference Vegetation Index (NDVI), land surface temperature (LST), and land use–land cover (LULC) variation from 2018 to 2023 with different satellites. The results of NDVI show an increasing trend with mean values rising from 0.30 in 2018 to 0.42 in 2023. Additionally, higher mean values of LST were noticed in 2020 by 14.4 °C and in 2022 by 14.1 °C. Furthermore, the SSR area has significantly changed, mostly in the eastern and southern regions of Jiangsu Province, from 2018 to 2023. The higher rice cropping area decreased by 1.42% in 2019 compared to 2018, while the total reduction over the 2018–2023 period was 0.92%. Total cultivated crop areas continued to decline because most of the crop areas changed into built-up areas, resulting in a total variation of 2.75% from 2020 to 2023. The overall accuracy of RF model range was 77.33% to 93.55% with a Kappa coefficient of 0.55 and 0.87, indicating moderate to substantial classification agreement across the study period. The results of LULC indicate that the crop area decreased by 4.13% from 2018 to 2023, and major areas were converted into water bodies and built areas. In conclusion, the single-season cropping pattern decreased during the study period, accompanied by a reduction in total cropland area in Jiangsu Province. Therefore, these findings highlight the influence of urbanization and climate change on agricultural land and emphasize adaptive strategies in Jiangsu Province to ensure food security in the face of environmental challenges.

The balance between gross primary productivity (GPP) and ecosystem respiration (ER) defines an ecosystem’s carbon sink-source status. Under global warming, hydrothermal conditions critically shape carbon fluxes, yet their differential impacts on GPP and ER remain insufficiently understood, especially across biomes. Here, based on flux observations, we selected mean annual temperature (MAT), latent heat flux (LH), vapor pressure deficit (VPD), soil water content (SWC), and annual precipitation (AP) as representative indicators of hydrothermal conditions, and employed mixed-effects models to examine how these key environmental drivers influence GPP and ER. After analyzing the fixed effects, LH and MAT promoted GPP more strongly than ER (slope = 0.5 > 0.253, slope = 0.595 > 0.392, respectively), whereas VPD suppressed GPP more than ER (slope = -0.658 < -0.499), yet accounted for a greater proportion of variance in ER than in GPP (R² = 0.14 > 0.07). Although SWC had a significant (p < 0.001) positive effect on GPP, the effect size was minimal, and its impact on ER was insignificant. R² decomposition showed that marginal R² values were similar for the GPP and ER models (0.43 and 0.44), whereas the GPP model exhibited a substantially higher conditional R² (0.82 compared to 0.63), indicating that MAT exerted a stronger influence on GPP than on ER across IGBP. The combined analysis of fixed and random effects indicated that MAT affected GPP more variably than ER across IGBP, with the strongest responses in mixed forests (MF) and savannas (SAV), intermediate responses in evergreen needleleaf forests (ENF), and the weakest responses in evergreen broadleaf forests (EBF). Overall, this study advances our understanding of how environmental factors differently influence GPP and ER, and incorporating these differences can improve predictions of ecosystem carbon fluxes and climate-carbon feedbacks.