Green vegetation plays a vital role in energy flows and matter cycles in terrestrial ecosystems, and vegetation phenology may not only be influenced by, but also impose active feedback on, climate changes. The phenological events of vegetation such as the start of season (SOS), end of season (EOS), and length of season (LOS) can respond to climate changes and affect gross primary productivity (GPP). Here, we coupled satellite remote sensing imagery with FLUXNET observations to systematically map the shift of SOS, EOS, and LOS in global vegetated area, and explored their response to climate fluctuations and feedback on GPP during the last two decades. The results indicated that 11.5% of the global vegetated area showed a significantly advanced trend in SOS, and that only 5.2% of the area presented significantly delayed EOS during the past two decades, resulting in significantly prolonged LOS in 12.6% of the vegetated area. The climate factors, including seasonal temperature and precipitation, attributed to the shifts in vegetation phenology, but with high spatial and temporal difference. LOS was positively and significantly correlated with GPP in 20.2% of the total area, highlighting that longer LOS is likely to promote vegetation productivity. The feedback on GPP from the shifted vegetation phenology may serve as an adaptation mechanism for terrestrial ecosystems to mitigate global warming through improved carbon uptake from the atmosphere.

Forests play a vital role in sequestering carbon dioxide from the atmosphere. Vegetation phenology is sensitive to climate changes and natural environments. Exploring the patterns in phenological events of the forests can provide useful insights for understanding the dynamics of vegetation growth and their responses to climate variations. Deciduous forest in North America is an important part of global forests. Here we apply time-series remote sensing imagery to map the critical dates of vegetation phenological events, including the start of season (SOS), end of season (EOS), and growth length (GL) of the deciduous forests in North America during the past two decades. The findings show that the SOS and EOS present considerable spatial and temporal variations. Earlier SOS, delayed EOS, and therefore extended GL are detected in a large part of the study area from temporal trend analysis over the years, though the magnitude of the trend varies at different locations. The phenological events are found to correlate to the environmental factors and the impact on the vegetation phenology from the factors is location-dependent. The findings confirm that the phenology of the deciduous forests in North America is updated such as advanced SOS and delayed EOS in the last two decades and the climate variations are likely among the driving forces for the updates. Considering that previous studies warn that shifts in vegetation phenology could reverse the role of forests as net emitters or net sinks, we suggest that forest management should be strengthened to forests that experience significant changes in the phenological events.

Green vegetation plays a vital role in urban ecosystem services. Rapid urbanization often tends to induce urban vegetation cover fragmentation (UVCF) in cities and suburbs. Mapping the changes in the structure (aggregation) and abundance of urban vegetation cover helps to make improved policies for sustainable urban development. In this paper, a new distance-based landscape indicator to UVCF, Frag, was proposed first. Unlike many other landscape indicators, Frag measures UVCF by considering simultaneously both the structure and abundance of vegetation cover at local scales, and thus provides a more comprehensive perspective in understanding the spatial distribution patterns in urban greenness cover. As a case study, the urban greenness fragmentation indicated by Frag was demonstrated in Wuhan metropolitan area (WMA), China in 2015 and 2020. Support vector machine (SVM) was then designed to examine the impact on the Frag changes from the associated factors, including urbanization and terrain characteristics (elevation and slope). The Frag changes were mapped at different scales and modeled by SVM from the selected factors, which reasonably explained the Frag changes. Sensitivity analysis for the SVM model revealed that urbanization showed the most dominant factor for the Frag changes, followed by terrain elevation and slope. We conclude that Frag is an effective scale-dependent indicator to UVCF that can reflect changes in the structure and abundance of urban vegetation cover, and that modeling the impact of the associated factors on UVCF via the Frag indicator can provide essential information for urban planners.

Rapid urbanization has threatened sustainable urban development in many cities across the globe, causing green space loss and vegetation cover degradation which reduce carbon sequestration. Optimal land management practices (LMPs) in an urban context are known as ways capable of promoting urban vegetation growth and contributing to carbon sequestration. Due to variations of physical, biological, and social structures in urban areas, policymakers often lack relevant information to decide and implement site-specific LMPs. Here we try to extract the areas in need of the optimal LMPs, identify location-dependent optimal LMPs, and assess how much more carbon can be captured by applying a combination of segmenting homogeneous urban environments and neighborhood-based analysis. As one of the most developed cities in China, the greater Guangzhou area (GGA) was selected as a case study. We found that the carbon uptake from the urban vegetation in GGA could be improved on average by 185 gC m⁻² yr⁻¹ in flux (or 1.3 TgC yr⁻¹ in total) with optimal LMPs, equivalent to a ~30% increase considering the current level of 662 gC m⁻² yr⁻¹ in flux (4.4 TgC yr⁻¹ in total). The carbon uptake potential was found to differ considerably across locations and among different ecosystem types, highlighting spatially varied priorities for implementing optimal LMPs over the space. This study reveals the usefulness of the model in assessing carbon uptake potential from optimal LMPs and emphasizes that future urban planning may consider the importance of optimal LMPs in enhancing vegetation carbon uptake in urban planning.

Accurate monitoring of paddy rice cropping intensity and planting dates across Monsoon Asia (MA) is crucial for food security and sustainable agriculture. While high-resolution (<30 m) maps are essential due to small field sizes in most parts of MA, there is currently no single high-resolution satellite-based paddy rice distribution, cropping intensity, or planting date map that covers the entire MA. This is due to primary challenges such as frequent cloud disturbance in high-resolution optical satellite images across subtropical and tropical MA and the interference from many factors other than rice transplanting in synthetic-aperture radar (SAR) backscatter signals, which has constrained the large-scale applicability of existing SAR-based mapping methods. To alleviate this constraint, we performed grid-specific checks on the location time, value, width, prominence, and sharpness of all troughs in SAR backscatter time series to remove those unrelated to rice transplanting. Thresholds for these checks are determined automatically when referring to optical satellite images, infrared remote-sensing-based land surface temperature, and passive microwave-based surface soil moisture. Based on this more universal algorithm, we created a satellite-based 20-m-resolution rice cropping intensity and planting date maps for the entire MA during 2018 to 2021. The rice distribution map achieved a high accuracy of 82% to 84% against 2,305 self-collected reference samples and 1,484 public samples, outperforming most existing regional maps. Our rice cropping intensity map generally agreed with existing regional maps, while the regional averages of our rice planting date estimates also correlated well with the statistics-based RiceAtlas dataset ( R ² : 0.92; root mean squared error: 28 d).