Enhancing terrestrial ecosystem carbon sinks is one of the effective strategies to achieve carbon neutrality targets for climate change mitigation. However, efforts to enhance the carbon sink mainly focused on natural climate solutions, including protecting, restoring, and managing terrestrial ecosystems. The contributions of artificial measures, such as rock weathering, urban alkaline material weathering, and desert saline alkali soil water to terrestrial ecosystem carbon sinks have often been overlooked by previous estimates of terrestrial carbon sinks. To account for major carbon sink measures, we proposed a new concept of “four-color” terrestrial carbons based on their formation/sink mechanisms, i.e., green carbon (the carbon sequestered by forest, grassland, and inland wetland), black carbon (carbon sink through the addition of carbon-rich materials, such as straw, organic fertilizers, and biochar to the soil), blue carbon (the carbon stored in coastal ecosystems), and white carbon (carbon sink by chemical processes). We reviewed the potential measures for enhancing the different carbon sinks to provide a framework for achieving carbon neutrality targets.We identified 15 measures for enhancing the sink of the different carbon groups. The enhancement approaches for green carbon sinks mainly include protecting, restoring, and managing forest, grassland, and inland wetland ecosystems. Adding carbon-rich materials to cropland soil is the major measure to enhance black carbon sink. The enhancement of blue carbon sink mainly focuses on ecosystem protection and restoration. We proposed enhancing silicate and carbonate rock weathering, irrigation and salt washing in arid regions, and utilizing urban alkaline materials as measures to enhance white carbon sink. With proper implementation of the above measures, we estimated C sequestration of 16.7 Pg CO2 yr-1 for green carbon, 5.8 Pg CO2 yr-1 for black carbon, 1.1 Pg CO2 yr-1 for blue carbon, and 7.7 Pg CO2 yr-1 for white carbon. Our results showed the potential to enhance the “four-color” carbon sinks globally by 31.4 Pg CO2 yr-1, higher than the estimate in the IPCC AR6. However, large uncertainties still exist in the estimation of current and future carbon sink potential in terrestrial ecosystems due to the different approaches used in different studies, large spatiotemporal variation, and insufficient data of carbon storage and sink. Therefore, we emphasize the need for strengthening monitoring and basic data acquisition and establishing rapid and accurate quantification techniques for terrestrial carbon sinks. Furthermore, future research should focus on the potential and multiple enhancement measures of white carbon under different climates and its response to global change.

Soil organic carbon (SOC) is a crucial indicator of soil quality and carbon cycling. While remote sensing and machine learning enable regional scale SOC prediction, most studies rely on vegetation indices (VIs) derived from bare-soil periods, potentially neglecting vegetation–soil interactions during crop growth. Given the bidirectional relationship between SOC and crop growth, we hypothesized that using crop-lush period VIs (VIs_lush) instead of bare-soil period VIs (VIs_bare) would increase the inversion accuracy. To test this hypothesis, we chose the cropland area in Liaoning Province as the study area and developed three modeling strategies (MS-1: VIs_lush + other features; MS-2: VIs_bare + other features; and MS-3: without VIs) using Landsat 8 imagery, topographic and precipitation data, and ensemble learning models (XGBoost, RF, and AdaBoost), with SHapley Additive exPlanations (SHAP) analysis for variable interpretation. We found that (1) all models achieved their highest performance under MS-1, with XGBoost outperforming the others across all modeling strategies; (2) for XGBoost, MS-1 yielded the highest inversion accuracy (R² = 0.84, RMSE = 2.22 g·kg⁻¹, RPD = 2.49, and RPIQ = 3.25); compared with MS-2, MS-1 reduced the RMSE by 0.31 g·kg⁻¹, increased R² from 0.77 to 0.84, and reduced the RPD by 0.31 and the RPIQ by 0.40, and compared with MS-3, MS-1 reduced the RMSE by 0.41 g·kg⁻¹, increased R² from 0.79 to 0.84, and reduced the RPD by 0.39 and the RPIQ by 0.51; (3) based on the SHAP analysis of the three modeling strategies, it is considered that precipitation, terrain and terrain analysis results are important indicators for SOC content inversion, and it is confirmed that VIs_lush contributed more than VIs_bare, supporting the rationale of using lush-period imagery; and (4) Liaoning Province exhibited distinct SOC spatial patterns (mean: 13.08 g·kg⁻¹), with values ranging from 2.19 g·kg⁻¹ (sandy central–western area) to 33.86 g·kg⁻¹ (eastern mountains/coast). This study demonstrates that integrating growth stage-specific VIs with ensemble learning can significantly enhance regional-scale SOC prediction.

Soil organic carbon (SOC) is a crucial indicator of soil quality and carbon cycling. While remote sensing and machine learning enable regional scale SOC prediction, most studies rely on vegetation indices (VIs) derived from bare-soil periods, potentially neglecting vegetation–soil interactions during crop growth. Given the bidirectional relationship between SOC and crop growth, we hypothesized that using crop-lush period VIs (VIs_lush) instead of bare-soil period VIs (VIs_bare) would increase the inversion accuracy. To test this hypothesis, we chose the cropland area in Liaoning Province as the study area and developed three modelling strategies (MS-1: VIs_lush + other features; MS-2: VIs_bare + other features; MS-3: without VIs) using Landsat 8 imagery, topographic and precipitation data, and ensemble learning models (XGBoost, RF, and AdaBoost), with SHapley Additive exPlanations (SHAP) analysis for variable interpretation. We found that 1) all models achieved their highest performance under MS-1, with XGBoost outperforming the others across all modelling strategies; 2) for XGBoost, MS-1 yielded the highest inversion accuracy (R² = 0.84, RMSE = 2.22 g·kg⁻¹, RPD = 2.49, and RPIQ = 3.25); compared with MS-2, MS-1 reduced the RMSE by 0.31 g·kg⁻¹, increased R² from 0.77 to 0.84, and reduced the RPD by 0.31 and the RPIQ by 0.40, and compared with MS-3, MS-1 reduced the RMSE by 0.41 g·kg⁻¹, increased R² from 0.79 to 0.84, and reduced the RPD by 0.39 and the RPIQ by 0.51; 2) SHAP analysis confirmed that VIs_lush contributed more than VIs_bare, supporting the rationale of using lush-period imagery; and 3) Liaoning Province exhibited distinct SOC spatial patterns (mean: 13.08 g·kg⁻¹), with values ranging from 2.19 g·kg⁻¹ (sandy central–western area) to 33.86 g·kg⁻¹ (eastern mountains/coast). This study demonstrates that integrating growth stage-specific VIs with ensemble learning can significantly enhance regional-scale SOC prediction.

The nitrogen (N) uptake preferences of mature tropical trees remain poorly understood, largely because traditional hydroponic methods fail to adequately simulate field conditions. To address this, we applied an in situ whole-tree paired ¹⁵N labeling experiment to quantify N acquisition strategies in three common species of tropical plantations in southern China: Hevea brasiliensis, Pinus caribaea, and Acacia mangium. The in situ whole-tree paired ¹⁵N labeling experiment revealed distinct species-specific nitrogen uptake strategies: Hevea brasiliensis preferentially absorbed nitrate (contributing 76% to total N uptake), Pinus caribaea relied more heavily on ammonium (61%), while Acacia mangium exhibited no strong preference for either N form but demonstrated the highest N uptake rate. These findings indicate the significant role of mycorrhizal type and biological nitrogen fixation in shaping N uptake patterns. Importantly, even when accounting for the dilution by the soil nitrogen pool, nitrate still contributed 42–99% of the total nitrogen uptake across the three tree species. All three species showed a substantial capacity for nitrate assimilation, challenging the conventional view of ammonium dominance in tropical trees and providing a mechanistic basis for refining nitrogen management practices in plantation forestry.

Soil acidity is one of the most serious constraints limiting crop productivity in regions with high rainfall in Ethiopia. To address this issue, field experiments were conducted during the 2024 main cropping season in farmers’ fields in two distinct ecological locations. This study aimed to explore the response of soil chemical properties and wheat (Triticum aestivum L.) to organic and inorganic amendments of acidic soils in Jaldu district and Cheha district in central Ethiopia. Application rates of 0, 2.5, 5, and 7.5 t/ha vermicompost; 0, 2, 4, and 6 t/ha agricultural lime; and 0, 50, 100, and 150 kg/ha NPS fertilizer were applied to acidic soils of the study areas. Soil chemical properties related to acidity were determined using standard laboratory procedures, which indicated low pH, available phosphorus (P), organic carbon (C), total nitrogen (N), total exchangeable bases, and cation exchange capacity, along with high exchangeable acid before planting. After harvesting, agricultural lime improved acidity-related soil conditions, including pH, organic C, available P, and total exchangeable bases, while reducing exchangeable acids in both districts. Vermicompost specifically improved soil organic C and total N in the soil of Jaldu district. The improvement in soil acidity due to liming was greater than that achieved by vermicompost or NPS fertilizer. Wheat yield increased in response to all rates of agricultural lime, vermicompost, and NPS-fertilizer applications. The highest yields of 5.71 t/ha and 4.62 t/ha were obtained with the application of 6 t/ha of agricultural lime in Cheha district and Jaldu district, respectively. The study demonstrated that sustainable agricultural practices soil amendment with agricultural inputs decreases soil acidity and improve wheat grain yields. Locational disparities in soil physical–chemical properties and wheat production with limited amendment options can limit the applicability of the findings from the selected study districts to wider regions. Therefore, investigations over diverse locations, seasons, and wheat varieties are needed to examine the effects on soil fertility and crop yield returns, and the economic viability of using diverse agricultural inputs.