Societal Impact Statement: Nitrogen, phosphorus, and potassium are essential elements for plant growth and are the primary nutrients in commercial fertilizers. However, the extent to which these nutrients individually limit plant growth is still unclear, as is the influence of their interactions. Using a meta‐analysis approach, plant growth was found to be co‐limited by nitrogen, phosphorus, and potassium. Furthermore, these findings demonstrate that multiple nutrient additions are primarily additive, with the exception of synergistic effects observed with NP additions on plant growth. These findings are important as they provide a timely and valuable reference for assessing the ecosystem‐scale consequences of increasing nutrient deposition caused by human activities.Summary: Numerous field studies and a few meta‐analyses have confirmed that terrestrial primary production is individually or jointly limited by nitrogen (N) and phosphorus (P). Meanwhile, a recent synthesis emphasized that potassium (K) limitation in terrestrial ecosystems is more widespread than previously thought. However, it remains unclear whether N, P, and K co‐limit terrestrial production and how their interactions influence plant growth. Therefore, we conducted a globally comprehensive synthesis of a total 2877 observations to explore the individual and interactive effects of N, P, and K additions on plant growth. We found that N, P, K, NP, NK, PK, and NPK additions significantly increased aboveground biomass production (AGP) by 30.8%, 15.3%, 12.3%, 68.4%, 36.5%, 21.1%, and 78.7%, respectively. The combined addition of multiple nutrients resulted in a larger increase in AGP than a single addition of nutrients. These findings suggested that terrestrial production is widely co‐limited by N, P, and K. However, no significant responses of belowground biomass production (BGP) to nutrient additions were observed. Except for synergistic interactions between N and P additions, the responses of AGP to other pairs of nutrient additions were predominantly additive. This meta‐analysis highlights the importance of multiple‐nutrient limitation in regulating terrestrial primary production while pointing out that non‐additive effects (including synergistic and antagonistic) of multiple‐nutrient additions on terrestrial production are relatively rare.

Accurate global monitoring of carbon dioxide (CO₂) is essential for understanding climate change and informing policy decisions. This study compares column-averaged dry-air mole fractions of CO₂ (XCO₂) between ACOS_L2_Lite_FP V9r for Japan’s Greenhouse Gases Observing Satellite (GOSAT), OCO-2_L2_Lite_FP V10r for the USA’s Orbiting Carbon Observatory-2 (OCO-2), and IAPCAS V2.0 for China’s Carbon Dioxide Observation Satellite (TANSAT) collectively referred to as GOT, with data from the Total Carbon Column Observing Network (TCCON). Our findings are as follows: (1) Significant data quantity differences exist between OCO-2 and the other satellites, with OCO-2 boasting a data volume 100 times greater. GOT shows the highest data volume between 30–45°N and 20–30°S, but data availability is notably lower near the equator. (2) XCO₂ from GOT exhibits similar seasonal variations, with lower concentrations during June, July, and August (JJA) (402.72–403.74 ppm) and higher concentrations during December, January, and February (DJF) (405.74–407.14 ppm). XCO₂ levels are higher in the Northern Hemisphere during March, April, and May (MAM) and DJF, while slightly lower during JJA and September, October, and November (SON). (3) The differences in XCO₂ (ΔXCO₂) reveal that ΔXCO₂ between OCO-2 and TANSAT are minor (−0.47 ± 0.28 ppm), whereas the most significant difference is observed between GOSAT and TANSAT (−1.13 ± 0.15 ppm). Minimal differences are seen in SON (with the biggest difference between GOSAT and TANSAT: −0.84 ± 0.12 ppm), while notable differences occur in DJF (with the biggest difference between GOSAT and TANSAT: −1.43 ± 0.17 ppm). Regarding latitudinal variations, distinctions between OCO-2 and TANSAT are most pronounced in JJA and SON. (4) Compared to TCCON, XCO₂ from GOT exhibits relatively high determination coefficients (R² > 0.8), with GOSAT having the highest root mean square error (RMSE = 1.226 ppm, <1.5 ppm), indicating a strong relationship between ground-based observed and retrieved values. This research contributes significantly to our understanding of the spatial characteristics of global XCO₂. Furthermore, it offers insights that can inform the analysis of differences in the inversion of carbon sources and sinks within assimilation systems when incorporating XCO₂ data from satellite observations.

In the face of rapid infrastructure expansion and escalating anthropogenic activities, it becomes imperative to prioritize the examination of long-term transformations in land cover and ecological quality within the Greater Mekong Subregion (GMS). We developed an ecological evaluation system integrating the land cover data assimilation framework (LCDAF) with the InVEST model to accomplish this goal. The LCDAF compensates for the disadvantages of weather interference, difficulty in recognizing complex scenes, and poor generalization in remote sensing image classification, and also adds temporal continuity that other fusion methods do not have. The synthesized land cover dataset demonstrates superior overall accuracy compared to five existing global products. This enhanced dataset provides a robust foundation for comprehensive analysis and decision making within the ecological evaluation system. We implemented a rigorous and quantitative assessment of changes in land cover and habitat quality spanning 1980 to 2020. The land cover analysis unveiled a noteworthy trend that surfaced in the dynamic interplay between forested areas and croplands, highlighting simultaneous processes of forest restoration and agricultural expansion, albeit at varying rates. Further analysis of habitat quality showed that the GMS generally sustained a moderate level with a slight downward trend observed over the period. Significantly, Laos attained the highest ranking in habitat quality, succeeded by Myanmar, China, Cambodia, Vietnam, and Thailand. In human factors, land use intensity and landscape fragmentation emerge as contributors with detrimental effects on habitat quality. Substantial progress was achieved in implementing forestland conservation measures, exemplified in regions such as Cambodia and Guangxi Province of China, where these endeavors proved effective in mitigating habitat degradation. Despite these positive endeavors, the GMS’s overall habitat quality did not significantly improve. It emphasizes the enduring challenges confronted by the region in terms of ecological management and habitat conservation.

Understanding the spatial distribution of soil organic matter (SOM) is important for land use management, but conventional sampling methods require significant human and financial resources. How to map SOM and monitor its changes using a limited number of sample points combined with remote sensing techniques that provide long-time series data is crucial. This study aimed to generate a regional-scale near-surface SOM map using 70 soil samples and covariate environmental factors extracted mainly from Landsat 8 OLI. Firstly, the sensitivity of each environmental factor to SOM was tested using a geographic detector model (GDM). Secondly, the tested factors were selected for modeling and mapping by ordinary least squares (OLS) and geographically weighted regression kriging (GWRK). The performance of these two models was compared. Finally, the mapping results of the better model (GWRK) were compared and analyzed with the traditional interpolation results based solely on sampling points to verify the rationality of the proposed method. The results show that three environmental factors, ratio vegetation index (RVI), differential vegetation index (DVI), and terrain roughness (TR), have a strong influence on the spatial variability of SOM. Using these three factors in combination with the GWRK method, a more accurate and refined spatial distribution map of SOM can be obtained. Comparing the SOM maps of GWRK and the traditional interpolation method, the results show that the accuracy of GWRK (R² = 0.405; mean absolute error = 0.637, and root mean square error = 0.813) is higher than that of traditional interpolation methods (R² = 0.291, MAE = 0.609, and RMSE = 0.863). The spatial recognition rate (fineness) of SOM patches at all levels using the GWRK method increased by more than 73 times compared to the traditional kriging. We conclude that the combination of limited SOM samples, environmental variables, GDM, and GWRK is a pragmatic approach for estimating regional-scale SOM.

Accurate evapotranspiration (ET) estimation is crucial for understanding ecosystem dynamics and managing water resources. Existing methodologies, including traditional techniques like the Penman–Monteith model, remote sensing approaches utilizing Solar-Induced Fluorescence (SIF), and machine learning algorithms, have demonstrated varying levels of effectiveness in ET estimation. However, these methods often face significant challenges, such as reliance on empirical coefficients, inadequate representation of canopy dynamics, and limitations due to cloud cover and sensor constraints. These issues can lead to inaccuracies in capturing ET’s spatial and temporal variability, highlighting the need for improved estimation techniques. This study introduces a novel approach to enhance ET estimation by integrating SIF partitioning with Photosynthetically Active Radiation (PAR) and leaf area index (LAI) data, utilizing the TL-LUE model (Two-Leaf Light Use Efficiency). Partitioning SIF data into sunlit and shaded components allows for a more detailed representation of the canopy’s functional dynamics, significantly improving ET modelling. Our analysis reveals significant advancements in ET modelling through SIF partitioning. At Xiaotangshan Station, the correlation between modelled ET and SIFsu is 0.71, while the correlation between modelled ET and SIFsh is 0.65. The overall correlation (R²) between the modelled ET and the combined SIF partitioning (SIF(P)) is 0.69, indicating a strong positive relationship at Xiaotangshan Station. The correlations between SIFsh and SIFsu with modelled ET show notable patterns, with R² values of 0.89 and 0.88 at Heihe Daman, respectively. These findings highlight the effectiveness of SIF partitioning in capturing canopy dynamics and its impact on ET estimation. Comparing modelled ET with observed ET and the Penman–Monteith model (PM model) demonstrates substantial improvements. R² values for modelled ET against observed ET were 0.68, 0.76, and 0.88 across HuaiLai, Shangqiu, and Yunxiao Stations. Modelled ET correlations to the PM model were 0.75, 0.73, and 0.90, respectively, at three stations. These results underscore the model’s capability to enhance ET estimations by integrating physiological and remote sensing data. This innovative SIF-partitioning approach offers a more nuanced perspective on canopy photosynthesis, providing a more accurate and comprehensive method for understanding and managing ecosystem water dynamics across diverse environments.