A leaf age-dependent light use efficiency model for remote sensing the gross primary productivity seasonality over pantropical evergreen broadleaved forests.

Tropical and subtropical evergreen broadleaved forests (TEFs) contribute more than one-third of terrestrial gross primary productivity (GPP). However, the continental-scale leaf phenology-photosynthesis nexus over TEFs is still poorly understood to date. This knowledge gap hinders most light use efficiency (LUE) models from accurately simulating the GPP seasonality in TEFs. Leaf age is the crucial plant trait to link the dynamics of leaf phenology with GPP seasonality. Thus, here we incorporated the seasonal leaf area index of different leaf age cohorts into a widely used LUE model (i.e., EC-LUE) and proposed a novel leaf age-dependent LUE model (denoted as LA-LUE model). At the site level, the LA-LUE model (average R2 = .59, average root-mean-square error [RMSE] = 1.23 gC m-2 day-1) performs better than the EC-LUE model in simulating the GPP seasonality across the nine TEFs sites (average R2 = .18; average RMSE = 1.87 gC m-2 day-1). At the continental scale, the monthly GPP estimates from the LA-LUE model are consistent with FLUXCOM GPP data (R2 = .80; average RMSE = 1.74 gC m-2 day-1), and satellite-based GPP data retrieved from the global Orbiting Carbon Observatory-2 (OCO-2) based solar-induced chlorophyll fluorescence (SIF) product (GOSIF) (R2 = .64; average RMSE = 1.90 gC m-2 day-1) and the reconstructed TROPOspheric Monitoring Instrument SIF dataset using machine learning algorithms (RTSIF) (R2 = .78; average RMSE = 1.88 gC m-2 day-1). Typically, the estimated monthly GPP not only successfully represents the unimodal GPP seasonality near the Tropics of Cancer and Capricorn, but also captures well the bimodal GPP seasonality near the Equator. Overall, this study for the first time integrates the leaf age information into the satellite-based LUE model and provides a feasible implementation for mapping the continental-scale GPP seasonality over the entire TEFs.

Dominant role of the non-forest woody vegetation in the post 2015/16 El Niño tropical carbon recovery.

The extreme dry and hot 2015/16 El Niño episode caused large losses in tropical live aboveground carbon (AGC) stocks. Followed by climatic conditions conducive to high vegetation productivity since 2016, tropical AGC are expected to recover from large losses during the El Niño episode; however, the recovery rate and its spatial distribution remain unknown. Here, we used low-frequency microwave satellite data to track AGC changes, and showed that tropical AGC stocks returned to pre-El Niño levels by the end of 2020, resulting in an AGC sink of 0.18 0.14 0.26 $$ {0.18}_{0.14}^{0.26} $$ Pg C year-1 during 2014-2020. This sink was dominated by strong AGC increases ( 0.61 0.49 0.84 $$ {0.61}_{0.49}^{0.84} $$ Pg C year-1) in non-forest woody vegetation during 2016-2020, compensating the forest AGC losses attributed to the El Niño event, forest loss, and degradation. Our findings highlight that non-forest woody vegetation is an increasingly important contributor to interannual to decadal variability in the global carbon cycle.

Linkages between stem vulnerability curves and tree demography and their implications for plant physiological modeling.

Vulnerability curves (VCs) have been measured extensively to describe the differences in plant vulnerability to cavitation. Although the roles of hydraulic conductivity (Ks,max) and hydraulic safety (P50, embolism resistance), both of which are parameters of VCs ('sigmoidal' type), in tree demography have been evaluated across different forests, the direct linkages between VCs and tree demography are rarely explored. In this study, we combined measured VCs and plot data of 16 tree species in Panamanian seasonal tropical forests to investigate the connections between VCs and tree mortality, recruitment and growth. We found that the mortality and recruitment rates of evergreen species were most significantly positively correlated with P50. However, the mortality and recruitment rates of deciduous species only exhibited significant positive correlations with parameter a, which describes the steepness of VCs and indicates the sensitivity of conductivity loss with water potential decline, but is often neglected. These differences among evergreen and deciduous species may contribute to the poor performance of existing quantitative relationships (such as the fitting relationships for all 16 species) in capturing tree mortality and recruitment dynamics. Additionally, evergreen species presented a significant positive relationship between relative growth rate (RGR) and Ks,max, while deciduous species did not display such relationship. The RGR of both evergreen and deciduous species also displayed no significant correlations with P50 and a. Further analysis demonstrated that species with steeper VCs tended to have high mortality and recruitment rates, while species with flatter VCs were usually those with low mortality and recruitment rates. Our results highlight the important role of parameter a in tree demography, especially for deciduous species. Given that VC is a key component of plant hydraulic models, integrating measured VC rather than optimizing its parameters will help improve the ability to simulate and predict forest response to water availability.

Four decades of full-scale nitrous oxide emission inventory in China.

China is among the top nitrous oxide (N2O)-emitting countries, but existing national inventories do not provide full-scale emissions including both natural and anthropogenic sources. We conducted a four-decade (1980-2020) of comprehensive quantification of Chinese N2O inventory using empirical emission factor method for anthropogenic sources and two up-to-date process-based models for natural sources. Total N2O emissions peaked at 2287.4 (1774.8-2799.9) Gg N2O yr-1 in 2018, and agriculture-developed regions, like the East, Northeast, and Central, were the top N2O-emitting regions. Agricultural N2O emissions have started to decrease after 2016 due to the decline of nitrogen fertilization applications, while, industrial and energetic sources have been dramatically increasing after 2005. N2O emissions from agriculture, industry, energy, and waste represented 49.3%, 26.4%, 17.5%, and 6.7% of the anthropogenic emissions in 2020, respectively, which revealed that it is imperative to prioritize N2O emission mitigation in agriculture, industry, and energy. Natural N2O sources, dominated by forests, have been steadily growing from 317.3 (290.3-344.1) Gg N2O yr-1 in 1980 to 376.2 (335.5-407.2) Gg N2O yr-1 in 2020. Our study produces a Full-scale Annual N2O dataset in China (FAN2020), providing emergent counting to refine the current national N2O inventories.

Upscaling net ecosystem CO2 exchanges in croplands: The application of integrating object-based image analysis and machine learning approaches.

Accurately estimating the net ecosystem exchange of CO2 (NEE) in cropland ecosystems is essential for understanding the impacts of agricultural practices and climate conditions. However, significant uncertainties persist in the estimation of regional cropland NEE due to landscape heterogeneity and variations in the efficacy of upscaling models. Here, we applied an integrated approach that combined object-based image analysis (OBIA) techniques with advanced machine learning (ML) approaches to upscale regional cropland NEE. We conducted a thorough evaluation of the upscaling approach across four distinct cropland areas characterized by diverse climate conditions. Our study confirmed that OBIA techniques can efficiently segment cropland objects, thereby enhancing the representation and accuracy of characteristics relevant to cropland features. The sequential least squares programming algorithm, among the three methods used for ML model integration, demonstrated exceptional performance in predicting NEE, with an R2 value exceeding 0.80 across all study areas and peaking at 0.90 in the most successful area. On average, there was an 18 % improvement compared to the poorest-performing ML model and a 6 % enhancement compared to the best-performing ML model. The upscaled regional products exhibited superior performance in characterizing cropland NEE patterns compared to pixel-based products. Additionally, we utilized the SHapley Additive exPlanations (SHAP) to assess driver importance, revealing that phenology and radiation had the greatest influence on prediction accuracy, followed by temperature and soil moisture. This study highlights the potential of integrating OBIA techniques with machine learning approaches for upscaling regional cropland NEE, while concurrently reducing estimation uncertainties.

Large potential of strengthening the land carbon sink in China through anthropogenic interventions.

The terrestrial ecosystem in China mitigates 21%-45% of the national contemporary fossil fuel CO2 emissions every year. Maintaining and strengthening the land carbon sink is essential for reaching China's target of carbon neutrality. However, this sink is subject to large uncertainties due to the joint impacts of climate change, air pollution, and human activities. Here, we explore the potential of strengthening land carbon sink in China through anthropogenic interventions, including forestation, ozone reduction, and litter removal, taking advantage of a well-validated dynamic vegetation model and meteorological forcings from 16 climate models. Without anthropogenic interventions, considering Shared Socioeconomic Pathways (SSP) scenarios, the land sink is projected to be 0.26-0.56 Pg C a-1 at 2060, to which climate change contributes 0.06-0.13 Pg C a-1 and CO2 fertilization contributes 0.08-0.44 Pg C a-1 with the stronger effects for higher emission scenarios. With anthropogenic interventions, under a close-to-neutral emission scenario (SSP1-2.6), the land sink becomes 0.47-0.57 Pg C a-1 at 2060, including the contributions of 0.12 Pg C a-1 by conservative forestation, 0.07 Pg C a-1 by ozone pollution control, and 0.06-0.16 Pg C a-1 by 20% litter removal over planted forest. This sink can mitigate 90%-110% of the residue anthropogenic carbon emissions in 2060, providing a solid foundation for the carbon neutrality in China.