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Icebergs are big chunks of ice floating on the ocean surface, and melting of icebergs contributes for the major part of freshwater flux into ocean. Dynamic monitoring of the icebergs in Antarctica and accurate estimation of their volume are important for predicting the trend of freshwater budget of the Southern Ocean. The iceberg freeboard is a key parameter for measuring the thickness and volume of an iceberg and is defined as the difference between the elevation of iceberg surface and sea level. So far, freeboards of icebergs have been successfully extracted using InSAR DEM, and the laser and radar altimeter. However, uncertainties exist in these results mainly caused by missed detection of small icebergs due to the spatially sparse and temporally incomplete data coverage. In addition to the above techniques, optical images can also be used to extract the iceberg freeboard based on its geometric relationship with shadow length, which can effectively compensate for the above shortcomings. Although the feasibility has been preliminarily presented, the precision and extensive application of shadow-height method deserves further research, such as estimating the basal melting of icebergs. In this work, we tested an optical image-based freeboard extraction method over icebergs in Prydz Bay, Antarctica. A normalized shadow pixel index (NSPI) is designed to identify iceberg shadows with different shapes in HY-1C/D CZI and Sentinel-2 MSI optical images. The iceberg freeboard can be determined with an acceptable precision (2 m) in optical images compared with laser altimeter (i.e. ICESat-2) measurements. Moreover, basal melting of icebergs has been assessed according to the variation of freeboard using repeated optical observations. The results indicate that icebergs in the study area were with a mean freeboard of about 56 m in early December 2022, and experienced a decrease in freeboard of 1.9 m within two months, in correspondence with the Antarctic seasonal trend. The methodological framework, therefore, turns out to be a reliable complementary approach to studying the iceberg freeboard in polar regions.

Abstract The two phases of El‐Niño‐Southern Oscillation (ENSO) influence both regional and global terrestrial vegetation productivity on inter‐annual scales. However, the major drivers for the regional vegetation productivity and their controlling strengths during different phases of ENSO remain unclear. We herein disentangled the impacts of two phases of ENSO on regional carbon cycle using multiple data sets. We found that soil moisture predominantly accounts for ∼40% of the variability in regional vegetation productivity during ENSO events. Our results showed that the satellite‐derived vegetation productivity proxies, gross primary productivity from data‐driven models (FLUXCOM) and observation‐constrained ecosystem model (Carbon Cycle Data Assimilation System) generally agree in depicting the contribution of soil moisture and air temperature in modulating regional vegetation productivity. However, the ensemble of weakly constrained ecosystem models exhibits non‐negligible discrepancies in the roles of vapor pressure deficit and radiation over extra‐tropics. This study highlights the significance of water in regulating regional vegetation productivity during ENSO.

Vegetation canopy water content (CWC) crucially affects stomatal conductance and photosynthesis and, consequently, is a key state variable in advanced ecosystem models. Remote sensing has been shown to be an effective tool for retrieving CWCs. However, the retrieval of the CWC from satellite remote sensing data is affected by the vegetation canopy structure and soil background. This study proposes a methodology that combines a modified spectral down-scaling model with a high-universality leaf water content inversion model to retrieve the CWC through constraining the impacts of canopy structure and soil background on CWC retrieval. First, canopy spectra acquired by satellite sensors were down-scaled to leaf reflectance spectra according to the probabilities of viewing the sunlit foliage (PT) and background (PG) and the estimated spectral multiple scattering factor (M). Then, leaf water content, or equivalent water thickness (EWT), was obtained from the down-scaled leaf reflectance spectra via a leaf-scale EWT inversion model calibrated with PROSPECT simulation data. Finally, the CWC was calculated as the product of the estimated leaf EWT and canopy leaf area index. Validation of this coupled model was performed using satellite-ground synchronous observation data across various vegetation types within the study area, affirming the model’s broad applicability. Results indicate that the modified spectral down-scaling model accurately retrieves leaf reflectance spectra, aligning closely with site-level measured spectra. Compared to the direct inversion approach, which performs poorly with Hyperion satellite images, the down-scale strategy notably excels. Specifically, the Similarity Water Index (SWI)-based canopy EWT coupled model achieved the most precise estimation, with a normalized Root Mean Square Error (nRMSE) of 15.28% and an adjusted R2 of 0.77, surpassing the performance of the best index Shortwave Angle Normalized Index (SANI)-based model (nRMSE = 15.61%, adjusted R2 = 0.52). Given its calibration using simulated data, this coupled model proved to be a potent method for extracting canopy EWT from satellite imagery, suggesting its applicability to retrieve other vegetative biochemical components from satellite data.

Abstract The magnitude and spatial pattern of anomalous net biome exchange (NBE) induced by the 2015/16 El Niño over Amazonian rainforests remain uncertain. We here investigated them using multi‐model posterior NBE products in the Orbiting Carbon Observatory‐2 (OCO‐2) version 10 modeling intercomparison project. Results suggest that relative to the annual NBE average in 2017/18, larger anomalous carbon release occurred over the eastern and northern Amazonian rainforests in 2015/16, with a total flux of approximately 0.4 PgC yr−1 after assimilating satellite‐observed column CO2 concentrations (XCO2) over land. We further find that this anomalous spatial pattern was predominantly determined by soil dryness, while the total positive NBE anomaly was dominated by higher temperature with its contribution of approximately 68～70%. We believe that atmospheric inversions assimilating more satellite‐observed XCO2 in future can provide us more comprehensive understanding how Amazonian rainforests cope with the abiotic stresses induced by El Niño events.

Anthropogenic nitrogen oxide (NO _x ) emissions are closely associated with human activities. In recent years, global human activity patterns have changed significantly owing to the COVID‐19 epidemic and international energy crisis. However, their effects on NO _x emissions are not yet fully understood. In this study, we developed a two-step inversion framework using NO _2 observations from the TROPOMI satellite and the GEOS-Chem global atmospheric chemical transport model, and inferred global anthropogenic NO _x emissions from 2019 to 2022, focusing on China, the United States (U.S.), and Europe. Our results indicated an 1.68% reduction in NO _x emissions in 2020 and a 5.72% rebound in 2021 across all regions. China rebounded faster than the others, surpassing its 2019 levels by July 2020. In 2022, emissions declined in all regions, driven mainly by the Omicron variant, energy shortages, and clean energy policies. Our findings provide valuable insights for the development of effective future emission management strategies.

Over the past decades, ecological restoration initiatives in China have made great progress in restoring degraded forests and increasing vegetation coverage, yet the carbon sequestration effects of these initiatives in the context of climate change are not clear. In this study, we assessed the effects of vegetation restoration on gross primary production (GPP) in China’s forestry engineering areas, where large-scale vegetation restoration programmes were launched, during 2001–2020 by disentangling the respective roles of land cover change (LCC), CO _2 fertilization, and climate changes using a two-leaf light use efficiency model. We found that LCC attributed by the vegetation restoration dominantly accelerated the increase of GPP in seven out of the eight areas, and CO _2 fertilization played a near-equivalent role in all areas. By contrast, the changes in different climate factors contributed to GPP variations diversely. The solar radiation variation greatly inhibited the vegetation GPP over time in seven out of these areas, and the changes in air temperature and vapor pressure deficit regulated GPP inter-annual variations without clear trends in all areas. This study advances our understanding of the contribution of China’s afforestation on its forest GPP in a changing climate, which may help to better manage forests to tackle the challenge of the climate crisis in the future.

Abstract Satellite XCO2 retrievals could improve the estimates of surface carbon fluxes, but it remains unknown on what scales these estimates are robust. Here, we use the time‐dependent Bayesian synthesis top‐down method and prior net ecosystem exchanges (NEEs) from 12 terrestrial biosphere models (TBMs) to infer the monthly carbon fluxes of 51 land regions with constraints by GOSAT XCO2 retrievals. We find that the uncertainty (standard deviation of 12 TBMs) reduction rates (uncertainty reduction rate (URR)) decrease significantly at decreasing spatial scales. On the continental‐scale, the mean URR is about 57%, and the annual and seasonal cycle estimates of NEE are rather robust. The evaluation shows that the posterior CO2 concentrations are significantly improved at the continental scale. Our study suggests that the GOSAT XCO2 can only promise a robust continental‐scale NEE estimate, and improving the XCO2 accuracy is an effective way to achieve robust estimates on smaller scales under current spatial coverage.

Remote sensing-driven light use efficiency (LUE) models have been widely used to calculate gross primary productivity (GPP) for various terrestrial ecosystems, but there was limited knowledge on the capacity of LUE models to evaluate the GPP in paddy rice ecosystems. In this study, at seven rice-growing sites over the Northern Hemisphere and based on six commonly used LUE models, we calibrated the parameters (i.e., maximum LUE (LUEmax) and optimum temperature) by separating the growing period into four phenological transitions and evaluated the performance of models, and investigated the impact of changes in cloud conditions and environmental factors (i.e., air temperature and vapor pressure deficit) on GPP simulations. The calibrated LUEmax corresponded closely to phenology, allowing the six LUE models to track the seasonal variations in GPP reasonably well. The sensitivity of GPP estimates to sky clearness index (CI) indicated that the TL-LUE model incorporating diffuse radiation fractions outperformed other models under cloudy conditions. Environmental stressors including along with changes in the diffuse radiation fractions synergistically affected GPP simulation, resulting in distinctly variable performances of the LUE models under different water and temperature conditions, with the TL-LUE model always performing well during the suitable rice growing season. These results demonstrate that it is crucial to consider the diffuse radiation fraction and to better represent environmental stresses under certain environmental conditions in LUE models for accurate estimation of rice GPP.

China, the Unite States (US), the European Union (EU), India, and Russia are the world's top 5 fossil fuel and cement CO2 (FFC) emitting countries or regions (CRs). It is very important to understand their status of carbon neutrality, and to monitor their future changes of net carbon fluxes (NCFs). In this study, we implemented a well-established global carbon assimilation system (GCAS, Version 2) to infer global surface carbon fluxes from May 2009 to December 2019 using both GOSAT and OCO-2 XCO2 retrievals. The reductions of flux uncertainty and XCO2 bias, and the evaluation of posterior flux show that GCAS has comparable and good performance in the 5 CRs. The results suggest that Russia has achieved carbon neutrality, but the other 4 are still far from being carbon neutral, especially China. The mean annual NCFs in China, the US, the EU, India, and Russia are 2.33 ± 0.29, 0.82 ± 0.20, 0.42 ± 0.16, 0.50 ± 0.12, and -0.33 ± 0.23 PgC yr−1, respectively. From 2010 to 2019, the NCFs showed an increasing trend in the US and India, a slight downward trend after 2013 in China, and were stable in the EU. The changes of land sinks in China and the US might be the main reason for their trends. India's trend was mainly due to the increase of FFC emission. The relative contributions of NCFs to the global land net carbon emission of China and the EU have decreased, while those of the US and India have increased, implying the US and India must take more active measures to control carbon emissions or increase their sinks. This study indicates that satellite XCO2 could be successfully used to monitor the changes of regional NCFs, which is of great significance for major countries to achieve greenhouse gas control goals.

Measurement(s) gross primary production of sunlit and shaded vegetation canopies Technology Type(s) the revised TL-LUE model Sample Characteristic - Environment carbon cycling Sample Characteristic - Location terrestrial ecosystem

Over the past 2 to 3 decades, Chinese forests are estimated to act as a large carbon sink, yet the magnitude and spatial patterns of this sink differ considerably among studies. Using 3 microwave (L- and X-band vegetation optical depth [VOD]) and 3 optical (normalized difference vegetation index, leaf area index, and tree cover) remote-sensing vegetation products, this study compared the estimated live woody aboveground biomass carbon (AGC) dynamics over China between 2013 and 2019. Our results showed that tree cover has the highest spatial consistency with 3 published AGC maps (mean correlation value R = 0.84), followed by L-VOD (R = 0.83), which outperform the other VODs. An AGC estimation model was proposed to combine all indices to estimate the annual AGC dynamics in China during 2013 to 2019. The performance of the AGC estimation model was good (root mean square error = 0.05 Pg C and R2 = 0.90 with a mean relative uncertainty of 9.8% at pixel scale [0.25°]). Results of the AGC estimation model showed that carbon uptake by the forests in China was about +0.17 Pg C year−1 from 2013 to 2019. At the regional level, provinces in southwest China including Guizhou (+22.35 Tg C year−1), Sichuan (+14.49 Tg C year−1), and Hunan (+11.42 Tg C year−1) provinces had the highest carbon sink rates during 2013 to 2019. Most of the carbon-sink regions have been afforested recently, implying that afforestation and ecological engineering projects have been effective means for carbon sequestration in these regions.

Sudden mega natural gas leaks of two Nord Stream pipelines in the Baltic Sea (Denmark) occurred from late September to early October 2022, releasing large amounts of methane into the atmosphere. We inferred the methane emissions of this event based on surface in situ observations using two inversion methods and two meteorological reanalysis datasets, supplemented with satellite-based observations. We conclude that approximately 220 ± 30 Gg of methane was released from September 26 to October 1, 2022.

Accurate representation of photosynthetic capacity and its seasonal variations is critical for modeling carbon sequestration of cropland ecosystems through photosynthesis. Previous studies indicated that the maximum carboxylation rate at 25 °C (Vcmax25) is the key determinant of photosynthetic capacity and can be mapped according to leaf nitrogen (N) and chlorophyll (Chl) contents. However, whether relationships of Vcmax25 with leaf nitrogen and chlorophyll contents in cropland ecosystems change over the whole growing season remain unclear. To address this question, we conducted intensive field campaigns in winter wheat and paddy rice farmlands of Eastern China, with leaf Vcmax25, the maximum electron transport rate at 25 °C (Jmax25), N, and Chl measured every 7 to 10 days from the tillering stage to maturity. Results showed that Vcmax25 and Jmax25 varied significantly during the growing seasons and maximized at flowering stages. For both winter wheat and paddy rice, Chla (Chl content at the area unit) showed significantly different relationships with Vcmax25 pre- and post- flowering while Na (N content at the area unit) did not. When different empirical models were used for pre- flowering and post- flowering stages, Vcmax25 estimated from Chla was closer to observations (R2 = 0.875, RMSE = 9.479 µmol m−2 s−1) than that estimated from Na (R2 = 0.565, RMSE = 17.718 µmol m−2 s−1). The findings in this study imply that the combination of remotely sensed leaf Chla content and phenology would improve the mapping of Vcmax25 for crops at regional and even global scales.

Drought has large impacts on the vegetation growth of global terrestrial ecosystems, particularly grasslands. Extensive in-situ studies have shown that the impact of drought on vegetation growth has lagged and cumulative effects, but it is not well known how grassland productivity (gross primary production or GPP) responds to drought over time at large scales. Here, we investigated the spatiotemporal patterns of lagged and cumulative effects of drought on global grassland GPP using an OCO-2 solar-induced chlorophyll fluorescence (SIF) based GPP product (GOSIF GPP) and the Standardized Precipitation Evapotranspiration Index (SPEI). The results showed that globally, 88.37% of the grassland had a lagged response to drought, with a lagged time scale of mainly 1 month. With the increase in soil water availability, the magnitude of the lagged effect gradually weakened. Grasslands in semiarid and semi humid conditions that were adaptable to periodic drought responded fastest to drought. Globally, more than three quarters (78.55%) of the grassland was affected by the cumulative effect of drought. The accumulated months occurred at longer time scales (8 ∼ 10 months) for the region at 40°N–55°N, which is relatively arid. The cumulative effect in relatively arid areas was stronger than that in relatively humid areas, indicating that grasslands in more arid areas had stronger tolerance to drought. More importantly, we found that the cumulative effect of drought on grassland GPP was stronger than the lagged effect. This study highlights the great importance of considering the legacy effect, especially the cumulative effect, of drought on grassland productivity, which would advance our understanding of the impact of climate change on the carbon and water cycles of terrestrial ecosystems.

China has implemented a series of large-scale afforestation projects to improve its ecological environment in recent decades. Meanwhile, the climate conditions in China have changed substantially. However, whether afforestation or climate change dominates vegetation gross primary production (GPP) in different stages of such afforestation projects is not clear. Here, taking the region of the Shelterbelt Program for Pearl River (SPPR) as the study area, we conducted six scenario simulations using the two-leaf light use efficiency (TL-LUE) model to decouple the effects of land cover change (LCC) and climate factors, i.e., solar radiation (Rad), vapor pressure deficit (VPD), and air temperature (Ta), on forest GPP, and disentangled the impact of various factors on forest GPP in Phase II (2001–2010) and Phase III (2011–2018). We found that industrialization and urbanization slightly changed the total forest area, partly offsetting the ecological benefits of the afforestation project in Phase II, and forest GPP increased slowly. As a result, Rad became the dominant factor of forest GPP increase (the cumulative contribution of 25.68 TgC in 10 years, accounting for 33.51%). In contrast, in Phase III, due to the further expansion of the afforestation area and the natural growth of earlier planted forests, the forest GPP increased rapidly, as did the contribution of LCC. Finally, LCC replaced Rad to become the primary contributor (the cumulative contribution of 114.42 TgC in 8 years, accounting for 60.51%) to the forest GPP increase. During the whole study period, Ta contributed insignificantly to the variation in forest GPP, while to some extent VPD decreased the forest GPP (−12.90% and −4.11% of the contributions in the two phases, respectively). The results suggest that the emergence of ecological benefits of afforestation projects requires a certain time length and forestation scale. Our study provides a basis for formulating effective land use policies.

TanSat is China’s first greenhouse gases observing satellite. In recent years, substantial progresses have been achieved on retrieving column-averaged CO2 dry air mole fraction (XCO2). However, relatively few attempts have been made to estimate terrestrial net ecosystem exchange (NEE) using TanSat XCO2 retrievals. In this study, based on the GEOS-Chem 4D-Var data assimilation system, we infer the global NEE from April 2017 to March 2018 using TanSat XCO2. The inversion estimates global NEE at −3.46 PgC yr-1, evidently higher than prior estimate and giving rise to an improved estimate of global atmospheric CO2 growth rate. Regionally, our inversion greatly increases the carbon uptakes in northern mid-to-high latitudes and significantly enhances the carbon releases in tropical and southern lands, especially in Africa and India peninsula. The increase of posterior sinks in northern lands is mainly attributed to the decreased carbon release during the nongrowing season, and the decrease of carbon uptakes in tropical and southern lands basically occurs throughout the year. Evaluations against independent CO2 observations and comparison with previous estimates indicate that although the land sinks in the northern middle latitudes and southern temperate regions are improved to a certain extent, they are obviously overestimated in northern high latitudes and underestimated in tropical lands (mainly northern Africa), respectively. These results suggest that TanSat XCO2 retrievals may have systematic negative biases in northern high latitudes and large positive biases over northern Africa, and further efforts are required to remove bias in these regions for better estimates of global and regional NEE.

The value of leaf photosynthetic capacity (Vcmax) varies with time and space, but state-of-the-art terrestrial biosphere models rarely include such Vcmax variability, hindering the accuracy of carbon cycle estimations on a large scale. In particular, while the European terrestrial ecosystem is particularly sensitive to climate change, current estimates of gross primary production (GPP) in Europe are subject to significant uncertainties (2.5 to 8.7 Pg C yr−1). This study applied a process-based Farquhar GPP model (FGM) to improve GPP estimation by introducing a spatially and temporally explicit Vcmax derived from the satellite-based leaf chlorophyll content (LCC) on two scales: across multiple eddy covariance tower sites and on the regional scale. Across the 19 EuroFLUX sites selected for independent model validation based on 9 plant functional types (PFTs), relative to the biome-specific Vcmax, the inclusion of the LCC-derived Vcmax improved the model estimates of GPP, with the coefficient of determination (R2) increased by 23% and the root mean square error (RMSE) decreased by 25%. Vcmax values are typically parameterized with PFT-specific Vcmax calibrated from flux tower observations or empirical Vcmax based on the TRY database (which includes 723 data points derived from Vcmax field measurements). On the regional scale, compared with GPP, using the LCC-derived Vcmax, the conventional method of fixing Vcmax using the calibrated Vcmax or TRY-based Vcmax overestimated the annual GPP of Europe by 0.5 to 2.9 Pg C yr−1 or 5 to 31% and overestimated the interannually increasing GPP trend by 0.007 to 0.01 Pg C yr−2 or 14 to 20%, respectively. The spatial pattern and interannual change trend of the European GPP estimated by the improved FGM showed general consistency with the existing studies, while our estimates indicated that the European terrestrial ecosystem (including part of Russia) had higher carbon assimilation potential (9.4 Pg C yr−1). Our study highlighted the urgent need to develop spatially and temporally consistent Vcmax products with a high accuracy so as to reduce uncertainties in global carbon modeling and improve our understanding of how terrestrial ecosystems respond to climate change.

Global warming has led to earlier spring green-up dates (GUDs) in recent decades with significant consequences for global carbon and hydrologic cycles. In addition to changes in climate, land cover change (LCC), including interchanges between vegetation and non-vegetation, and among plants with different functional traits, may also affect GUD. Here, we analyzed how satellite-derived GUD from 1992 to 2020 was impacted by changes in temperature, precipitation, standardized precipitation evapotranspiration index (SPEI), solar radiation, and LCC for the Northern Hemisphere (>30° N). While the climate variables had larger impact overall, variability in GUD was controlled by LCC for 6% of the Northern Hemisphere, with systematically earlier or later changes among transitions between different land cover types. These changes were found mainly along the southeastern coast of the United States, in Central-north Europe, and across northeastern China. We further showed that climate change attribution of earlier GUD during 1992–2020 was overestimated by three days when the impact of LCC was ignored. Our results deepen the understanding of how LCC impacts GUD variability and enables scientists to more accurately evaluate the impact of climate change on land surface phenology.

Precipitation plays a dominant role in regulating terrestrial carbon fluxes. In concert with global warming, aridity has been increasing during recent decades in most parts of the world. How global terrestrial carbon fluxes respond to this change, however, is still unclear. Using a remote-sensing-driven, process-based model, the Boreal Ecosystem Productivity Simulator (BEPS), this study investigated the responses of global terrestrial carbon fluxes to meteorological drought, which were characterized by the standardized precipitation evapotranspiration index (SPEI). The results showed that the response of terrestrial carbon fluxes to drought exhibited distinguishable spatial heterogeneity. In most regions, terrestrial carbon fluxes responded strongly to drought. With an increase in annual water balance (annual precipitation minus annual potential evapotranspiration), the response of carbon fluxes to drought became weaker. The lagged time of terrestrial carbon fluxes responding to drought decreased with the increasing strength of carbon fluxes in response to drought. The sensitivity of terrestrial carbon fluxes to drought also showed noticeable spatial heterogeneity. With an increase in annual water balance, the sensitivity first increased and then decreased. Terrestrial carbon fluxes exhibited the highest sensitivity to drought in semi-arid areas.

Due to the substantial gross exchange fluxes with the atmosphere, the terrestrial carbon cycle plays a significant role in the global carbon budget. Drought commonly affects terrestrial carbon absorption negatively. Terrestrial biosphere models exhibit significant uncertainties in capturing the carbon flux response to drought, which have an impact on estimates of the global carbon budget. Through plant physiological processes, soil moisture tightly regulates the carbon cycle in the environment. Therefore, accurate observations of soil moisture may enhance the modeling of carbon fluxes in a model–data fusion framework. We employ the Carbon Cycle Data Assimilation System (CCDAS) to assimilate 36-year satellite-derived surface soil moisture observations in combination with flask samples of atmospheric CO2 concentrations. We find that, compared to the default model, the performance of optimized net ecosystem productivity (NEP) and gross primary productivity (GPP) has increased with the RMSEs reduced by 1.62 gC/m2/month and 10.84 gC/m2/month, which indicates the added value of the ESA-CCI soil moisture observations as a constraint on the terrestrial carbon cycle. Additionally, the combination of soil moisture and CO2 concentration in this study improves the representation of inter-annual variability of terrestrial carbon fluxes as well as the atmospheric CO2 growth rate. We thereby investigate the ability of the optimized GPP in responding to drought by comparing continentally aggregated GPP with the drought index. The assimilation of surface soil moisture has been shown to efficiently capture the influences of the sub-annual (≤9 months drought durations) and large-scale (e.g., regional to continental scales) droughts on GPP. This study highlights the significant potential of satellite soil moisture for constraining inter-annual models of the terrestrial biosphere’s carbon cycle and for illustrating how GPP responds to drought at a continental scale.

Pantana phyllostachysae Chao (PPC) is one of the deadliest defoliators of Moso bamboo. Accurately locating and evaluating PPC damage is essential for the management of bamboo forests. Moso bamboo has a unique biennial growth cycle, consisting of the on-year period (bamboo shoots are incubated and then produced) and the off-year period (old leaves are dropped and then new leaves are grown, and no bamboo shoots are produced in the coming year). The similar physiological characteristics of off-year bamboo and damaged on-year bamboo create difficulties in monitoring PPC damage using remote sensing data. In this study, we synergistically used Sentinel-1, Sentinel-2, and field inventory data to construct machine learning (extreme gradient boosting, XGBoost) models monitoring PPC damage. The results show that the single-time observation feature-based model (using images from October) outperformed the double-time observation feature-based model (using the differences between remote sensing signals from October and February or April) due to the interference from other disturbance agents (e.g., logging and weeding). The overall accuracy (OA) values of the single-time observation feature-based model were at least 3% and 10% higher than those for double-time observation feature-based models for on- and off-year samples, respectively. With the consideration of the on- and off-year phenological differences, OA was improved by over 4%. The model without differentiation of the phenological difference tended to underestimate the damaged area of on-year bamboo and overestimate that of off-year bamboo. We also found that the responses of optical and SAR (synthetic aperture radar) features to PPC damage were different. The optical features increased or decreased with increasing damage severity. SAR features decreased significantly at the initial stage of PPC damage and then changed marginally with the increase in damage severity. The addition of SAR features to optical features improved the model performance, mainly for healthy and mildly damaged samples. The methodology developed in this study provides technical and theoretical support for the pest monitoring of bamboo forests using remote sensing data.

There lacks systematic analysis on the importance of vegetation structural change in the global terrestrial carbon cycle. Here the authors conducted a multi-model comparison analysis and find that the increase in leaf area index has been responsible for 12.4% of the accumulated terrestrial carbon sink from 1981 to 2016.

Abstract Grazing activity is a fundamental behavior in pasture ecosystems and, globally, is a major disturbance that leads to destruction of terrestrial biomass. However, its impact on ecosystem C sequestration at large scales is not well understood due to its obvious anthropogenic property. In this study, we proposed a Data‐Process combined Grazing Scheme (DPGS) to quantify the regional grazing impact on ecosystem C sequestration in the typical pasture ecosystem, Temperate Eurasian Steppe. First, a pixel‐based livestock distribution map was generated based on fine‐scale (province/prefecture) inventory data using a resource‐oriented livestock distribution approach. Then the C consumption due to grazing (Closs,graze) was simulated by combining a late version of a remote‐sensing‐based terrestrial model, the Boreal Ecosystem Productivity Simulator and the Shiyomi grazing model. The modeled regional livestock density was evaluated against the Gridded Livestock of the World data set. The DPGS was able to reproduce the spatial distribution of livestock. Because extralarge herbivores (camel and horse) were involved in the calculation, the DPGS predicts higher livestock density than the Gridded Livestock of the World data set over 70% of the region. The modeled Closs,graze and its seasonal variability were validated against multiple site‐based data sets. The results showed good agreements with the field observations of Closs,graze. With further tests and data incorporations, this scheme has the potential to produce high‐resolution data sets of livestock distribution and Closs,graze and become a useful diagnostic instrument for model evaluation, parameterization, and intercomparison.

Abstract Indian Ocean Dipole (IOD), a major climate variability in the tropics which drives the abiotic stress associated with heavy rainfalls and severe droughts, is not much understood in terms of its role in the carbon cycle, while El Niño‐Southern Oscillation (ENSO)‐related terrestrial carbon cycle variation has been intensively studied. Here, we investigate IOD's impact on land photosynthesis over the Indian ocean rim countries during austral spring using satellite‐based gross primary productivity (GPP) and Earth System Model simulations produced in the Coupled Model Intercomparison Project Phase 6 (CMIP6). IOD independently affects GPP with significant positive partial correlation coefficients (pcor) over most of Africa and India, and negative pcor over southern China, Indo‐China peninsula, maritime continent, and Australia, mostly driven by precipitation variations; this obviously differs from the widespread significant negative pcor pattern induced by ENSO. The recent extremely positive IOD in 2019 caused the canonical IOD‐affected GPP patterns, however, with its extreme impacts. Furthermore, though large inter‐model spreads exist, the CMIP6 multimodel median can basically capture the main characteristics of IOD‐affected precipitation and GPP patterns. Importantly, IOD is predicted to occur more frequently in future warming scenarios. Model future projections suggest that it will exert larger impacts on GPP variations over central and eastern Africa, Sumatra, western and southeastern Australia with stronger pcor and enhanced explained variance, but less impacts over southern Africa, east India, Indo‐China peninsula, and northeastern Australia. Therefore, besides ENSO, understanding the IOD impacts can provide us new insights into regional and global carbon cycle interannual variability.

Remote sensing of solar-induced chlorophyll fluorescence (SIF) provides new possibilities to estimate terrestrial gross primary production (GPP). To mitigate the angular and canopy structural effects on original SIF observed by sensors (SIFobs), it is recommended to derive total canopy SIF emission (SIFtotal) of leaves within a canopy using canopy interception (i0) and reflectance of vegetation (RV). However, the effects of the uncertainties in i0 and RV on the estimation of SIFtotal have not been well understood. Here, we evaluated such effects on the estimation of GPP using the Soil-Canopy-Observation of Photosynthesis and the Energy balance (SCOPE) model. The SCOPE simulations showed that the R2 between GPP and SIFtotal was clearly higher than that between GPP and SIFobs and the differences in R2 (ΔR2) tend to decrease with the increasing levels of uncertainties in i0 and RV. The resultant ΔR2 decreased to zero when the uncertainty level in i0 and RV was ~30% for red band SIF (RSIF, 683 nm) and ~20% for far-red band SIF (FRSIF, 740 nm). In addition, as compared to the TROPOspheric Monitoring Instrument (TROPOMI) SIFobs at both red and far-red bands, SIFtotal derived using any combination of i0 (from MCD15, VNP15, and CGLS LAI products) and RV (from MCD34, MCD19, and VNP43 BRDF products) showed comparable improvements in estimating GPP. With this study, we suggest a way to advance our understanding in the estimation of a more physiological relevant SIF datasets (SIFtotal) using current satellite products.

The maximum rate of carboxylation (Vcmax), a key parameter indicating photosynthetic capacity, is commonly fixed as a constant by vegetation types and/or varies according to empirical scaling functions in Earth system models (ESMs). As such, the setting of Vcmax results in uncertainties of estimated carbon assimilation. It is known that the coupling between leaf chlorophyll and Rubisco (ribulose-1,5-biphosphate carboxylase-oxygenase) contents can be applied to estimate Vcmax. However, how this coupling is affected by environmental changes and varies among plant functional types (PFTs) has not been well investigated yet. The effect of varying coupling between chlorophyll and Rubisco contents on the estimation of Vcmax is still not clear. In this study, we compiled data from 76 previous studies to investigate the coupling between Chlorophyll (Chl) and Rubisco (Rub), in different PFTs and under different environmental conditions. We also assessed the ability of a Rub-based semi-mechanistic model to estimate Vcmax normalized to 25 °C (Vcmax _25 ) based on the Rub–Chl relationship. Our results revealed strong, linear Rub-Chl relationships for different PFTs ( R ^2 = 0.73, 0.67, 0.54 and 0.72 for forest, crop, grass and shrub, and C4 plants, respectively). The Rub–Chl slope of natural C3 PFTs was consistent and significantly different from those of crops and C4 plants. A meta-analysis indicated that reduced light intensity, elevated CO _2 , and nitrogen addition strongly altered Rub/Chl. A semi-mechanistic model based on PFT-specific Rub–Chl relationships was able to estimate Vcmax _25 with high confidence. Our findings have important implications for improving global carbon cycle modeling by ESMs through the improved parameterization of Vcmax _25 using remotely sensed Chl content.

Effectively monitoring Pantana phyllostachysae Chao (PPC) is essential for the sustainable development of the bamboo industry. However, the morphological similarity between damaged and off-year bamboo imposes challenges in the monitoring. The knowledge on whether the severity of this pest could be effectively monitored by using remote sensing methods is very limited. To fill this gap, this study aimed to identify the PPC damage of moso bamboo leaves using hyperspectral data. Specifically, we investigated differences in relative chlorophyll content (RCC), leaf water content (LWC), leaf nitrogen content (LNC), and hyperspectral spectrum among healthy, damaged (mildly damage, moderately damage, severely damage), and off-year bamboo leaves. Then, the hyperspectral indices sensitive to pest damage were selected by recursive feature elimination (RFE). The PPC damage identification model was constructed using the light gradient boosting machine (LightGBM) algorithm. We designed two different scenarios, without (A) and with (B) off-year samples, to evaluate the impact of off-year leaves on identification results. The RCC, the LWC, and the LNC of damaged leaves generally showed clear declined trends with the deterioration of damaged severity. The RCC and the LNC of off-year leaves were significantly lower than those of healthy and damaged leaves, whereas the LWC of off-leaves was significantly different from that of damaged leaves. The pest infestation caused noticeable distortion of leaf spectrum, increases in red and shortwave infrared bands, and decreases in green and near-infrared bands. The magnitude of reflectance change increased with the pest severity. The reflectance of off-year leaves in visible and near-infrared regions was distinguishably higher than that of healthy and damaged leaves. The overall accuracy (OA) of the constructed model for the identification of leaves with different degrees of damage severity reached 81.51%. When off-year, healthy, and damaged leaves were lumped together, the OA of the constructed model decreased by 5%. About half of the off-year leaf samples were misclassified into the damaged group. The identification of off-year leaves is a challenge for monitoring PPC damage using hyperspectral data. These results can provide practical guidance for monitoring PPC using remote sensing methods.

Continuous increases in the human population and human activities have resulted in remarkable changes in the composition of the atmosphere since the industrial revolution [...]

The CO _2 efflux from soil (soil respiration (SR)) is one of the largest fluxes in the global carbon (C) cycle and its response to climate change could strongly influence future atmospheric CO _2 concentrations. Still, a large divergence of global SR estimates and its autotrophic (AR) and heterotrophic (HR) components exists among process based terrestrial ecosystem models. Therefore, alternatively derived global benchmark values are warranted for constraining the various ecosystem model output. In this study, we developed models based on the global soil respiration database (version 5.0), using the random forest (RF) method to generate the global benchmark distribution of total SR and its components. Benchmark values were then compared with the output of ten different global terrestrial ecosystem models. Our observationally derived global mean annual benchmark rates were 85.5 ± 40.4 (SD) Pg C yr ^−1 for SR, 50.3 ± 25.0 (SD) Pg C yr ^−1 for HR and 35.2 Pg C yr ^−1 for AR during 1982–2012, respectively. Evaluating against the observations, the RF models showed better performance in both of SR and HR simulations than all investigated terrestrial ecosystem models. Large divergences in simulating SR and its components were observed among the terrestrial ecosystem models. The estimated global SR and HR by the ecosystem models ranged from 61.4 to 91.7 Pg C yr ^−1 and 39.8 to 61.7 Pg C yr ^−1 , respectively. The most discrepancy lays in the estimation of AR, the difference (12.0–42.3 Pg C yr ^−1 ) of estimates among the ecosystem models was up to 3.5 times. The contribution of AR to SR highly varied among the ecosystem models ranging from 18% to 48%, which differed with the estimate by RF (41%). This study generated global SR and its components (HR and AR) fluxes, which are useful benchmarks to constrain the performance of terrestrial ecosystem models.

Ecosystem models provide an effective approach to quantify the terrestrial carbon cycle, but the lack of accurate phenological information prevents them from better simulations of the physical processes. Compared with spring phenology (i.e. the start of the growing season, SOS), the vegetation phenology in autumn (the end of the growing season, EOS) is not well-simulated and it is challenging to incorporate vegetation phenology into ecosystem models. The simulation of EOS based on temperature and photoperiod was widely accepted, such as Delpierre et al (2009 Agric. For. Meteorol. 149 938–48)’s model (DM), yet its accuracy has not been fully discussed at a regional scale. Here, we developed a regional autumn phenological model (DMS) with inputs of temperature, photoperiod, and water availability for China’s terrestrial ecosystems. The new DMS model significantly improved the representation of EOS in terms of the lower root mean square error (RMSE), higher model efficiency, and a higher percentage of significant correlation with the referenced EOS. We observed widespread delaying trends of EOS with an average rate of 0.1 d yr ^−1 for vegetated areas over 2001–2018. We further incorporated the improved EOS into the boreal ecosystem productivity simulator (BEPS) and found that the phenology-modified BEPS model had better performances in predicting annual gross primary productivity (GPP) with ∼28% lower RMSE than the original model when testing against GPP measurements from flux tower sites. From 2001 to 2017, the interannual GPP simulated by the modified BEPS model showed an increasing trend with a rate of 6.0 g C m ^−2 yr ^−2 . In conclusion, our study proves that water availability is of great significance for modeling autumn phenology, and the incorporation of phenological dates into an ecosystem model is helpful for productivity simulation.

Light use efficiency (LUE) models are widely used to estimate gross primary productivity (GPP), a dominant component of the terrestrial carbon cycle. Their outputs are very sensitive to LUE. Proper determination of this parameter is a prerequisite for LUE models to simulate GPP at regional and global scales. This study was devoted to investigating the ability of the photochemical reflectance index (PRI) to track LUE variations for a sub-tropical planted coniferous forest in southern China using tower-based PRI and GPP measurements over the period from day 101 to 275 in 2013. Both half-hourly PRI and LUE exhibited detectable diurnal and seasonal variations, and decreased with increases of vapor pressure deficit (VPD), air temperature (Ta), and photosynthetically active radiation (PAR). Generally, PRI is able to capture diurnal and seasonal changes in LUE. However, correlations of PRI with LUE varied dramatically throughout the growing season. The correlation was the strongest (R2 = 0.6427, p < 0.001) in July and the poorest in May. Over the entire growing season, PRI relates better to LUE under clear or partially cloudy skies (clearness index, CI > 0.3) with moderate to high VPD (>20 hPa) and high temperatures (>31 C). Overall, we found that PRI is most sensitive to variations in LUE under stressed conditions, and the sensitivity decreases as the growing conditions become favorable when atmosphere water vapor, temperature and soil moisture are near the optimum conditions.

Solar radiation significantly affects terrestrial gross primary productivity (GPP). However, the relationship between GPP and solar radiation is nonlinear because it is affected by diffuse radiation. Solar radiation has undergone a shift from darker to brighter values over the past 30 years in China. However, the effects on GPP of variation in solar radiation because of changes in diffuse radiation are unclear. In this study, national global radiation in conjunction with other meteorological data and remotely sensed data were used as input into a two-leaf light use efficiency model (TL-LUE) that simulated GPP separately for sunlit and shaded leaves for the period from 1981 to 2012. The results showed that the nationwide annual global radiation experienced a significant reduction (2.18 MJ m−2 y−1; p < 0.05) from 1981 to 2012, decreasing by 1.3% over this 32-year interval. However, the nationwide annual diffuse radiation increased significantly (p < 0.05). The reduction in global radiation from 1981 to 2012 decreased the average annual GPP of terrestrial ecosystems in China by 0.09 Pg C y−1, whereas the gain in diffuse radiation from 1981 to 2012 increased the average annual GPP in China by about 50%. Therefore, the increase in canopy light use efficiency under higher diffuse radiation only partially offsets the loss of GPP caused by lower global radiation.

Laser return intensity (LRI) information obtained from airborne laser scanning (ALS) data has been used to classify land cover types and to reveal canopy physiological features. However, the sensor-related and environmental parameters may introduce noise. In this study, we developed a local median filtering (LMF) method to point-by-point correct the LRI information. For each point, we deduced the reference variation range for its LRI. Then, we replaced the outliers of LRI with their local median values. To evaluate the LMF method, we assessed the discrepancy of LRI information from the same and diverse land cover types. Moreover, we used the corrected LRI to distinguish points from grass, road, and bare land, which were classified as ground type in ALS data. The results show that using the LMF method could increase the similarity of pointwise LRI from the same land cover type and the discrepancy of those from different kinds of targets. Using the LMF-corrected LRI could improve the overall classification accuracy of three land cover types by about 3% (all over 81%, κ ≥ 0.73, p < 0.05), compared to those using the original and range-normalized LRI. The sensor-related metrics brought more noise to the original LRI information than the environmental factors. Using the LMF method could effectively correct LRI information from historical ALS datasets.

Satellite sun-induced chlorophyll fluorescence (SIF) has emerged as a promising tool for monitoring growing conditions and productivity of vegetation. However, it still remains unclear the ability of satellite SIF data to predict crop yields at the regional scale, comparing to widely used satellite vegetation index (VI), such as the Enhanced Vegetation Index (EVI) from the Moderate Resolution Imaging Spectroradiometer (MODIS). Additionally, few attempts have been made to verify if SIF products from the new Orbiting Carbon Observatory-2 (OCO-2) satellite could be applied for regional corn and soybean yield estimates. With the deep neural networks (DNN) approach, this study investigated the ability of OCO-2 SIF, MODIS EVI, and climate data to estimate county-level corn and soybean yields in the U.S. Corn Belt. Monthly mean and maximum SIF and MODIS EVI during the peak growing season showed similar correlations with corn and soybean yields. The DNNs with SIF as predictors were able to estimate corn and soybean yields well but performed poorer than MODIS EVI and climate variables-based DNNs. The performance of SIF and MODIS EVI-based DNNs varied with the areal dominance of crops while that of climate-based DNNs exhibited less spatial variability. SIF data could provide useful supplementary information to MODIS EVI and climatic variables for improving estimates of crop yields. MODIS EVI and climate predictors (e.g., VPD and temperature) during the peak growing season (from June to August) played important roles in predicting yields of corn and soybean in the Midwestern 12 states in the U.S. The results highlighted the benefit of combining data from both satellite and climate sources in crop yield estimation. Additionally, this study showed the potential of adding SIF in crop yield prediction despite the small improvement of model performances, which might result from the limitation of current available SIF products. The framework of this study could be applied to different regions and other types of crops to employ deep learning for crop yield forecasting by combining different types of remote sensing data (such as OCO-2 SIF and MODIS EVI) and climate data.

The 2019–20 Australian mega-bushfires, which raged particularly over New South Wales and Victoria, released large amounts of toxic haze and CO _2 . Here, we investigate whether the resulting CO _2 enhancement can be directly detected by satellite observations, based on National Aeronautics and Space Administration’s Orbiting Carbon Observatory-2 (OCO-2) column-averaged CO _2 (XCO _2 ) product. We find that smoke from wildfires can greatly obscure satellite observations, making the available XCO _2 mainly locate over outer fringes of plumes downwind of the major mega-bushfires in eastern Australia in three orbit observations during November–December 2019, with their enhancements of approximately 1.5 ppm. This fire-induced CO _2 enhancement is further confirmed using an atmospheric transport model, Goddard Earth Observing System-Chem, forced by satellite observation-derived fire product Global Fire Emissions Database, version 4.1 and wind observations, with comparable simulated XCO _2 enhancements. Model simulation also suggests that the sensitivity of the downwind maximum XCO _2 enhancement is 0.41 $ \pm \,$ 0.04 ppm for 1 TgC d ^−1 fire emissions. In sum, though detectable to some extent, it remains a challenge to get the accurate maximum XCO _2 enhancements due to the gaps in XCO _2 detections obscured by smoke. Understanding the capability of OCO-2 XCO _2 detection is prerequisite for monitoring and constraining wildfire CO _2 emissions by inversions.

The reliable simulation of gross primary productivity (GPP) at various spatial and temporal scales is of significance to quantifying the net exchange of carbon between terrestrial ecosystems and the atmosphere. This study aimed to verify the ability of a nonlinear two-leaf model (TL-LUEn), a linear two-leaf model (TL-LUE), and a big-leaf light use efficiency model (MOD17) to simulate GPP at half-hourly, daily and 8-day scales using GPP derived from 58 eddy-covariance flux sites in Asia, Europe and North America as benchmarks. Model evaluation showed that the overall performance of TL-LUEn was slightly but not significantly better than TL-LUE at half-hourly and daily scale, while the overall performance of both TL-LUEn and TL-LUE were significantly better (p < 0.0001) than MOD17 at the two temporal scales. The improvement of TL-LUEn over TL-LUE was relatively small in comparison with the improvement of TL-LUE over MOD17. However, the differences between TL-LUEn and MOD17, and TL-LUE and MOD17 became less distinct at the 8-day scale. As for different vegetation types, TL-LUEn and TL-LUE performed better than MOD17 for all vegetation types except crops at the half-hourly scale. At the daily and 8-day scales, both TL-LUEn and TL-LUE outperformed MOD17 for forests. However, TL-LUEn had a mixed performance for the three non-forest types while TL-LUE outperformed MOD17 slightly for all these non-forest types at daily and 8-day scales. The better performance of TL-LUEn and TL-LUE for forests was mainly achieved by the correction of the underestimation/overestimation of GPP simulated by MOD17 under low/high solar radiation and sky clearness conditions. TL-LUEn is more applicable at individual sites at the half-hourly scale while TL-LUE could be regionally used at half-hourly, daily and 8-day scales. MOD17 is also an applicable option regionally at the 8-day scale.

Urban sprawl/urbanization has large impacts on the structure and function of terrestrial ecosystems. Net primary production (NPP) is an important indicator for estimating the earth’s ability to support life and aids the evaluation of sustainable development of the terrestrial ecosystem. In this study, the process-based boreal ecosystem productivity simulator (BEPS) model was used in conjunction with leaf area index (LAI) dataset, land cover, and meteorological and soil data to simulate daily NPP at spatial resolution 250 m in Nanjing, a representative region within the Yangtze Delta, for the period 2001–2010. Effects of urbanization on land-cover change and regional NPP are quantitatively evaluated. The results show that during this period, urbanization caused significant land-cover change. Compared with 2001, urbanized area and area covered by water bodies increased significantly, while vegetated area declined greatly. The greatest loss was cropland, followed by evergreen coniferous and closed deciduous forests. There were obvious spatial differences in NPP variations. The reduction rate of annual NPP in the major city of Nanjing, Jiangning District, and Gaochun County was much higher than that in Pukou and Luhe district, and Lishui County. These results indicate that a process-based model driven by remote sensing is useful in assessing the impact of urban sprawl on NPP, and urbanization, not climate factors, is a main factor for NPP reduction for an urbanizing region.

Water-use efficiency (WUE), defined as the ratio of net primary productivity (NPP) to evapotranspiration (ET), is an important indicator to represent the trade-off pattern between vegetation productivity and water consumption. Its dynamics under climate change are important to ecohydrology and ecosystem management, especially in the drylands. In this study, we modified and used a late version of Boreal Ecosystem Productivity Simulator (BEPS), to quantify the WUE in the typical dryland ecosystems, Temperate Eurasian Steppe (TES). The Aridity Index (AI) was used to specify the terrestrial water availability condition. The regional results showed that during the period of 1999-2008, the WUE has a clear decreasing trend in the spatial distribution from arid to humid areas. The highest annual average WUE was in dry and semi-humid sub-region (DSH) with 0.88 gC mm-1 and the lowest was in arid sub-region (AR) with 0.22 gC mm-1. A two-stage pattern of WUE was found in TES. That is, WUE would enhance with lower aridity stress, but decline under the humid environment. Over 65% of the region exhibited increasing WUE. This enhancement, however, could not indicate that the grasslands were getting better because the NPP even slightly decreased. It was mainly attributed to the reduction of ET over 70% of the region, which is closely related to the rainfall decrease. The results also suggested a similar negative spatial correlation between the WUE and the mean annual precipitation (MAP) at the driest and the most humid ends. This regional pattern reflected the different roles of water in regulating the terrestrial ecosystems under different aridity levels. This study could facilitate the understanding of the interactions between terrestrial carbon and water cycles, and thus contribute to a sustainable management of nature resources in the dryland ecosystems.

Seasonal variations of photosynthetic capacity parameters, notably the maximum carboxylation rate, Vcmax, play an important role in accurate estimation of CO2 assimilation in gas-exchange models. Satellite-derived normalised difference vegetation index (NDVI), enhanced vegetation index (EVI) and model-data fusion can provide means to predict seasonal variation in Vcmax. In this study, Vcmax was obtained from a process-based model inversion, based on an ensemble Kalman filter (EnKF), and gross primary productivity, and sensible and latent heat fluxes measured using eddy covariance technique at two deciduous broadleaf forest sites and a mixed forest site. Optimised Vcmax showed considerable seasonal and inter-annual variations in both mixed and deciduous forest ecosystems. There was noticeable seasonal hysteresis in Vcmax in relation to EVI and NDVI from 8 d composites of satellite data during the growing period. When the growing period was phenologically divided into two phases (increasing VIs and decreasing VIs phases), significant seasonal correlations were found between Vcmax and VIs, mostly showing R2>0.95. Vcmax varied exponentially with increasing VIs during the first phase (increasing VIs), but second and third-order polynomials provided the best fits of Vcmax to VIs in the second phase (decreasing VIs). The relationships between NDVI and EVI with Vcmax were different. Further efforts are needed to investigate Vcmax–VIs relationships at more ecosystem sites to the use of satellite-based VIs for estimating Vcmax.

Leaf chlorophyll content plays a vital role in plant photosynthesis. The PROSPECT model has been widely used for retrieving leaf chlorophyll content from remote sensing data over various plant species. However, despite wide variations in leaf surface reflectance across different plant species and environmental conditions, leaf surface reflectance is assumed to be the same for different leaves in the PROSPECT model. This work extends the PROSPECT model by taking into account the variation of leaf surface reflection. In the modified model named PROSPECT-Rsurf, an additional surface layer with a variable refractive index is bounded on the N elementary layers. Leaf surface reflectance (Rs) is characterized by the difference between the refractive indices of leaf surface and interior layers. The specific absorption coefficients of the leaf total chlorophyll and carotenoids were recalibrated using a cross-calibration method and the refractive indices of leaf surface and interior layers were obtained during model inversion. Chlorophyll content (Cab) retrieval and spectral reconstruction in the visible spectral region (VIS, 400−750 nm) were greatly improved using PROSPECT-Rsurf, especially for leaves covered by heavy wax or hard cuticles that lead to high surface reflectance. The root mean square error (RMSE) of chlorophyll estimates decreased from 11.1 µg/cm2 to 8.9 µg/cm2 and the Pearson’s correlation coefficient (r) increased from 0.81 to 0.88 (p < 0.01) for broadleaf samples in validation, compared to PROSPECT-5. For needle leaves, r increased from 0.71 to 0.89 (p < 0.01), but systematic overestimation of Cab was found due to the edge effects of needles. After incorporating the edge effects in PROSPECT-Rsurf, the overestimation of Cab was alleviated and its estimation was improved for needle leaves. This study explores the influence of leaf surface reflectance on Cab estimation at the leaf level. By coupling PROSPECT-Rsurf with canopy models, the influence of leaf surface reflectance on canopy reflectance and therefore canopy chlorophyll content retrieval can be investigated across different spatial and temporal scales.

China’s terrestrial ecosystems play an important role in the global carbon cycle. Regional contributions to the interannual variation (IAV) of China’s terrestrial carbon sink and the attributions to climate variations are not well understood. Here we have investigated how terrestrial ecosystems in the four climate zones with various climate variabilities contribute to the IAV in China’s terrestrial net ecosystem productivity (NEP) using modeled carbon fluxes data from six ecosystems models. Model results show that the monsoonal region of China dominates national NEP IAV with a contribution of 86% (69%–96%) on average. Yearly national NEP changes are mostly driven by gross primary productivity IAV and half of the annual variation results from NEP changes in summer. Regional contributions to NEP IAV in China are consistent with their contributions to the magnitude of national NEP. Rainfall variability dominates the NEP annual variability in China. Precipitation in the temperate monsoon climate zone makes the largest contribution (23%) to the IAV of NEP in China because of both the high sensitivity of terrestrial ecosystem carbon uptake to rainfall and the large fluctuation in the precipitation caused by the East Asian summer monsoon anomalies. Our results suggest that NEP IAV can be mainly attributed to ecosystems with larger productivity and response to precipitation, and highlight the importance of monsoon climate systems with high seasonal and interannual variability in driving internannual variation in the land carbon sink.

The spring sensible heating (SH) over the Tibetan Plateau (TP) serves as a huge ‘air pump’, significantly influencing the Asian summer monsoon, has experienced a decreasing trend. However, it remains unclear whether this decline will continue. Therefore, we here examine the long-term trends of spring SH over the central and eastern TP (CETP) based on a meteorological station-based calculated SH dataset, and CMIP6 multi-model simulations. These two sources confirmed the previous finding that the SH peaks in May. Further, we find that the declining SH was replaced by a fast recovery after approximate 2000 in the station-based SH. This is to some extent verified by the historical simulations of CMIP6 models. Importantly, CMIP6 future projections suggest that this increasing trend will continue, and get stronger with higher radiative forcing from SSP126 to SSP585. Mechanism analysis indicates that the previous decreasing trend in SH was mainly caused by the decline of 10 m wind speed, while the recent and future increasing trend results from the rising ground-air temperature difference. We suggest that this increasing trend of spring SH over the CETP may serve as an alternative driver for the enhancement of the East Asian summer monsoon in the future.

China’s forests have functioned as important carbon sinks. They are expected to have substantial future potential for biomass carbon sequestration (BCS) resulting from afforestation and reforestation. However, previous estimates of forest BCS have included large uncertainties due to the limitations of sample size, multiple data sources, and inconsistent methodologies. This study refined the BCS estimation of China’s forests from 2010 to 2050 using the national forest inventory data (FID) of 2009−2013, as well as the relationships between forest biomass and stand age retrieved from field observations for major forest types in different regions of China. The results showed that biomass⁻age relationships were well-fitted using field data, with respective R2 values more than 0.70 (p < 0.01) for most forest types, indicating the applicability of these relationships developed for BCS estimation in China. National BCS would increase from 130.90 to 159.94 Tg C year−1 during the period of 2010−2050 because of increases in forest area and biomass carbon density, with a maximum of 230.15 Tg C year−1 around 2030. BCS for young and middle-aged forests would increase by 65.35 and 15.38 Tg C year−1, respectively. 187.8% of this increase would be offset by premature, mature, and overmature forests. During the study period, forest BCS would increase in all but the northern region. The largest contributor to the increment would be the southern region (52.5%), followed by the southwest, northeast, northwest, and east regions. Their BCS would be primarily driven by the area expansion and forest growth of young and middle-aged forests as a result of afforestation and reforestation. In the northern region, BCS reduction would occur mainly in the Inner Mongolia province (6.38 Tg C year−1) and be caused predominantly by a slowdown in the increases of forest area and biomass carbon density for different age⁻class forests. Our findings are in broader agreement with other studies, which provide valuable references for the validation and parameterization of carbon models and climate-change mitigation policies in China.

Drought impacts carbon and water fluxes of terrestrial ecosystems, which are strongly coupled. However, the magnitudes of response of carbon and water fluxes to drought are dependent on many processes, which are more complex than previously expected. Southern China experienced regional climatic perturbation events in the past decade and a two-year drought in 2009–2010. We used a terrestrial ecosystem model coupled with remotely sensed observations and metrological data to simulate the variations of net primary productivity (NPP), evapotranspiration (ET), and water-use efficiency (WUE) (i.e., NPP/ET) in south-western China during the period 2001–2010. Using the standard precipitation index (SPI) classifying different drought stresses, we also quantified the effect of drought on the ecosystem by comparing changes in modelled estimates of monthly WUE, NPP and ET under normal (i.e., baseline) and drought conditions (i.e., 2009 and 2010). The results indicated that NPP and ET showed synchronized declines in drought periods, with time-lag effects. Furthermore, drought-induced NPP decline was larger than ET reduction. An increasing trend in WUE from the moderate to extreme drought classes occurred not only in baseline conditions but also in drought conditions. Especially in the extreme drought period (January, 2010), WUE for the forest ecosystem typically showed a positive response to drought, indicating a drought-resilient forest ecosystem. Our study has important implications for understanding climate extreme effects on the carbon and water cycle of the forest ecosystem.

The impact of grazing activity on terrestrial carbon (C) sequestration has been noticed and studied worldwide. Recent efforts have been made to incorporate the disturbance into process-based land models. However, the performance of grazing models has not been well investigated at large scales. In this study, we performed a spatially explicit model uncertainty assessment in the world’s largest pasture ecosystem, the temperate Eurasian Steppe. Five grazing models were explicitly incorporated into a single terrestrial biogeochemical model to simulate regional C consumption from grazing activity ( C _graze ). First, we summarized the underlying mechanisms and explicitly compared the general functions used to describe the processes in different models. Then, the models (five models with 12 simulations) were run in parallel using the same forcing data and livestock distribution map in 2006. Results indicated that the modeled regional C _graze varied from 0.1–16.1 gC m ^−2 for the year. The corresponding ratios of C _graze to aboveground net primary productivity ANPP and net primary productivity (NPP) ranged from 0.08%–24.6% and 0.028%–11.2%, respectively. Parameter sensitivity was further analyzed. Model outputs are highly sensitive to the intake rate (i.e. feeding rate of livestock per day), half maximum intake rate, and initial livestock weight. Our results indicate that great uncertainty exists in simulating C _graze . We ascribed the major uncertainty to the different process description and poor parameterization. This study calls for more efforts to the comprehensive synthesis of usable dataset, the foundation of a standard observation system and the observe-based inter-comparison to evaluate models, which would facilitate more accurate assessment of C sequestration by pasture ecosystems and lead to better representation in earth system models.

Leaf Incorporating Biochemistry Exhibiting Reflectance and Transmittance Yields (LIBERTY) models the effects of leaf biochemical concentrations on reflectance spectra on the basis of Melamed theory, which has several limitations. These are: (1) the radiation components are not treated satisfactorily; (2) the directional changes of both particle and sublayer scattering ratios are not considered; and (3) the boundary constraint which makes needle leaves different from broadleaves is not included. Proofs of these limitations as well as theoretical improvements are given in this study. Global sensitivity analysis (SA) of three models: the original LIBERTY, our improved LIBERTY (LIBERTYim) and The optical PROperties SPECTra model (PROSPECT) suggests that compared with LIBERTY, the global reflectance and transmittance of LIBERTYim are more sensitive to diametrical absorbance α d —a parameter related to leaf biochemistry. Moreover, the global reflectance and transmittance of LIBERTYim and PROSPECT had similar sensitivity patterns to the input variables, demonstrating indirectly the validity of our improvements over LIBERTY. However, neither LIBERTY nor LIBERTYim considers boundary constraints, which limits their applications in modelling needle leaf optical properties. We introduced a particle string model, which might be used to simulate needle leaf optical properties in the future.

Nitrogen is an essential nutrient in many terrestrial ecosystems because it affects vegetation’s primary production. Due to the variety of nitrogen-containing substances and the differences in their composition across species, statistical approaches are now dominant in remote sensing retrieval of leaf nitrogen content. Many studies remove spectral regions characterized by strong water absorptions before retrieving nitrogen content, because water is believed to mask the absorption features of nitrogen. The objectives of this study are to discuss the necessity of this practice and to explore how water absorption affects leaf nitrogen estimation. Spectral measurements and chemical analyses for Maize, Sawtooth Oak, and Sweetgum leaves were carried out in 2014. The leaf optical properties model PROSPECT5 was used to eliminate the influences of water on the measured reflectance spectra. The inversion accuracy of PROPECT5 for chlorophyll, carotenoid, water, and dry matter of Maize was also discussed. Measured, simulated, and water-removed spectra were used to: (1) find the optimal nitrogen-related spectral index; and (2) regress with the area-based leaf nitrogen concentration (LNC) using the partial least square regression technique (PLSR). Two types of spectral indices were selected in this study: Normalized Difference Spectral Index (NDSI) and Ratio Spectral Index (RSI). Additionally, first-order derivative forms of measured, simulated, and water-removed spectra were devised to search for the optimal spectral indices. Finally, species-specific optimal indices and cross-species optimal indices, as well as their root mean square errors (RMSE) and coefficients of determination (R2), were obtained. The Ending Top Percentile (ETP), an indicator of the performance of cross-species optimal indices, was also calculated. PLSR was combined with leave-one-out cross validation (LOOCV) for each species. The predicted root mean square errors (RMSEP) and predicted R2 were finally calculated. The results showed that chlorophyll, carotenoid, and water contents could be estimated with R2 of 0.75, 0.59, and 0.69, respectively, which were acceptable for fresh leaves. The dry matter was retrieved with a relatively lower accuracy because of the fixed absorption coefficients adopted by PROSPECT5. The performances of species-specific optimal indices using water-free spectra were comparable to or worse than the corresponding indices derived with measured or simulated spectra. Compared with measured spectra, ETP did not change much after the effects of water were removed, and the R2 between cross-species optimal spectral indices and area-based LNC for Sawtooth Oak and Sweetgum decreased while it remained almost the same for Maize, suggesting that the water-removed cross-species optimal indices were inferior to the corresponding optimal indices found without water removal. ETP was larger than 30% for all spectra, demonstrating the non-existence of common optimal NDSI or RSI for the three species. After water removal, the accuracy of PLSR for Sawtooth Oak and Sweetgum decreased and increased negligibly for Maize. The results suggest that water absorption has limited effects on reducing the accuracy of leaf nitrogen estimation. On the contrary, the accuracy may decrease due to the loss of spectral information caused by the removal of water-sensitive spectral regions.

The temperate Eurasian steppe (TES) is a region where various environmental, social, and economic stresses converge. Multiple types of disturbance exist widely across the landscape, and heavily influence carbon cycling in this region. However, a current quantitative assessment of the impact of disturbances on carbon sequestration is largely lacking. In this study, we combined the boreal ecosystem productivity simulator (BEPS), the Shiyomi grazing model, and the global fire model (Glob-FIRM) to investigate the impact of the two major types of disturbance in the TES (i.e. domestic grazing and fire) on regional carbon sequestration. Model performance was validated using satellite data and field observations. Model outputs indicate that disturbance has a significant impact on carbon sequestration at a regional scale. The annual total carbon lost due to disturbances was 7.8 TgC yr ^−1 , accounting for 14.2% of the total net ecosystem productivity (NEP). Domestic grazing plays the dominant role in terrestrial carbon consumption, accounting for 95% of the total carbon lost from the two disturbances. Carbon losses from both disturbances significantly increased from 1999 to 2008 ( R ^2 = 0.82, P < 0.001 for grazing, R ^2 = 0.51, P

A new moisture adjusted vegetation index (MAVI) is proposed using the red, near infrared, and shortwave infrared (SWIR) reflectance in band-ratio form in this paper. The effectiveness of MAVI in retrieving leaf area index (LAI) is investigated using Landsat-5 data and field LAI measurements in two forest and two grassland areas. The ability of MAVI to retrieve forest LAI under different background conditions is further evaluated using canopy reflectance of Jack Pine and Black Spruce forests simulated by the 4-Scale model. Compared with several commonly used two-band vegetation index, such as normalized difference vegetation index, soil adjusted vegetation index, modified soil adjusted vegetation index, optimized soil adjusted vegetation index, MAVI is a better predictor of LAI, on average, which can explain 70% of variations of LAI in the four study areas. Similar to other SWIR-related three-band vegetation index, such as modified normalized difference vegetation index (MNDVI) and reduced simple ratio (RSR), MAVI is able to reduce the background reflectance effects on forest canopy LAI retrieval. MAVI is more suitable for retrieving LAI than RSR and MNDVI, because it avoids the difficulty in properly determining the maximum and minimum SWIR values required in RSR and MNDVI, which improves the robustness of MAVI in retrieving LAI of different land cover types. Moreover, MAVI is expressed as ratios between different spectral bands, greatly reducing the noise caused by topographical variations, which makes it more suitable for applications in mountainous area.