Sea surface Fresnel reflections difference driven de-glint algorithm for airborne optical images.

This paper presents a glint correction algorithm for high spatial resolution optical remote sensing imagery captured by the ER-2 Airborne Visual Infrared Imaging Spectrometer (AVIRIS). The algorithm employs linear and differential techniques to mitigate sun glint and sky glint effects, encompassing statistical glint reflections resulting from variations in imaging angles within strips and inter-strip variations due to Fresnel reflectance disparities. It aims to diminish Fresnel reflectance diversity on water surfaces and mitigate the distortions induced by glint reflectance during spectral and ocean color inversion. A comparative analysis of spectral and ocean color information in AVIRIS images before and after correction reveals enhanced accuracy following the glint correction. By systematically addressing multiple glint reflections and their ramifications, this method offers a valuable framework for correcting water surface glint in diverse high spatial resolution optical imagery.

China's Fossil Fuel CO2 Emissions Estimated Using Surface Observations of Coemitted NO2.

Accurate estimates of fossil fuel CO2 (FFCO2) emissions are of great importance for climate prediction and mitigation regulations but remain a significant challenge for accounting methods relying on economic statistics and emission factors. In this study, we employed a regional data assimilation framework to assimilate in situ NO2 observations, allowing us to combine observation-constrained NOx emissions coemitted with FFCO2 and grid-specific CO2-to-NOx emission ratios to infer the daily FFCO2 emissions over China. The estimated national total for 2016 was 11.4 PgCO2·yr-1, with an uncertainty (1σ) of 1.5 PgCO2·yr-1 that accounted for errors associated with atmospheric transport, inversion framework parameters, and CO2-to-NOx emission ratios. Our findings indicated that widely used "bottom-up" emission inventories generally ignore numerous activity level statistics of FFCO2 related to energy industries and power plants in western China, whereas the inventories are significantly overestimated in developed regions and key urban areas owing to exaggerated emission factors and inexact spatial disaggregation. The optimized FFCO2 estimate exhibited more distinct seasonality with a significant increase in emissions in winter. These findings advance our understanding of the spatiotemporal regime of FFCO2 emissions in China.

The carbon budget of China: 1980-2021.

As one of the world's largest emitters of greenhouse gases, China has set itself the ambitious goal of achieving carbon peaking and carbon neutrality. Therefore, it is crucial to quantify the magnitude and trend of sources and sinks of atmospheric carbon dioxide (CO2), and to monitor China's progress toward these goals. Using state-of-the-art datasets and models, this study comprehensively estimated the anthropogenic CO2 emissions from energy, industrial processes and product use, and waste along with natural sources and sinks of CO2 for all of China during 1980-2021. To recognize the differences among various methods of estimating greenhouse emissions, the estimates are compared with China's National Greenhouse Gas Inventories (NGHGIs) for 1994, 2005, 2010, 2012, and 2014. Anthropogenic CO2 emissions in China have increased by 7.39 times from 1980 to 12.77 Gt CO2 a-1 in 2021. While benefiting from ecological projects (e.g., Three Norths Shelter Forest System Project), the land carbon sink in China has reached 1.65 Gt CO2 a-1 averaged through 2010-2021, which is almost 15.81 times that of the carbon sink in the 1980s. On average, China's terrestrial ecosystems offset 14.69% ± 2.49% of anthropogenic CO2 emissions through 2010-2021. Two provincial-level administrative regions of China, Xizang and Qinghai, have achieved carbon neutrality according to our estimates, but nearly half of the administrative regions of China have terrestrial carbon sink offsets of less than 10% of anthropogenic CO2 emissions. This study indicated a high level of consistency between NGHGIs and various datasets used for estimating fossil CO2 emissions, but found notable differences for land carbon sinks. Future estimates of the terrestrial carbon sinks of NGHGIs urgently need to be verified with process-based models which integrate the comprehensive carbon cycle processes.

Improving sea surface floating matter identification from Sentinel-2 MSI imagery using optical radiative simulation of neighborhood difference.

The reflectance difference (ΔR) between a floating matter pixel and a nearby water reference pixel is a method of atmospheric radiation unmixing. This technique unveils target signals by referencing the background within the horizontal neighborhood. ΔR is effective for removing the mixed-pixel effect and partial atmospheric path radiance. However, other atmospheric interference sources in the difference pixel, including atmospheric extinction and sunglint, need to be clarified. To address these challenges, we combined in situ floating matter endmember spectra for simulation and Sentinel-2 Multispectral Instrument (MSI) sensors for validation. We focused on radiative transfer simulation of horizontal neighborhood and vertical atmospheric column, investigating the bilateral conversion of ΔR between bottom-of-atmosphere (BOA) and top-of-atmosphere (TOA) signals, and clarifying how the atmosphere affects the difference pixel (ΔR) and floating matter identification. Results showed that direct use of TOA ΔR works in discriminating algae from non-algae floating matters under weak sunglint, and is a suitable candidate for no bother with atmospheric correction, least uncertain, and wider coverage. And then, sunglint interference is also inevitable, whether serious or not.

Large diurnal compensatory effects mitigate the response of Amazonian forests to atmospheric warming and drying.

Photosynthesis and evapotranspiration in Amazonian forests are major contributors to the global carbon and water cycles. However, their diurnal patterns and responses to atmospheric warming and drying at regional scale remain unclear, hindering the understanding of global carbon and water cycles. Here, we used proxies of photosynthesis and evapotranspiration from the International Space Station to reveal a strong depression of dry season afternoon photosynthesis (by 6.7 ± 2.4%) and evapotranspiration (by 6.1 ± 3.1%). Photosynthesis positively responds to vapor pressure deficit (VPD) in the morning, but negatively in the afternoon. Furthermore, we projected that the regionally depressed afternoon photosynthesis will be compensated by their increases in the morning in future dry seasons. These results shed new light on the complex interplay of climate with carbon and water fluxes in Amazonian forests and provide evidence on the emerging environmental constraints of primary productivity that may improve the robustness of future projections.

Sunglint reflection facilitates performance of spaceborne UV sensor in oil spill detection.

Ultraviolet Imager (UVI) onboard Haiyang-1C/D (HY-1C/D) satellites has been providing ultraviolet (UV) data to detect marine oil spills since 2018. Although the scale effect of UV remote sensing has been preliminarily interpreted, the application characteristics of spaceborne UV sensors with medium spatial resolution in oil spill detection deserve further investigation, especially the role of sunglint in the process of detection. In this study, the performance of the UVI is thoroughly assessed by the following aspects: image features of oils under sunglint, sunglint requirement for spaceborne UV detection of oils, and the stability of the UVI signal. The results indicate that in UVI images, it is sunglint reflection that determines the image features of spilled oils, and the appearance of sunglint can strengthen the contrast between oils and seawater. Besides, the required sunglint strength in spaceborne UV detection has been deduced to be 10-3 - 10-4 sr-1, which is higher than that in the VNIR wavelengths. Moreover, uncertainties in the UVI signal can meet the demand to discriminate between oils and seawater. The above results can confirm the capability of the UVI and the critical role of sunglint in spaceborne UV detection of marine oil spills, and provide new reference for spaceborne UV remote sensing.

Correction of multi-scale sunglint reflections from the water surface in airborne high-spatial resolution optical images.

Airborne optical images (AOI) are often with complex sunglint reflections, which brings a certain influence to watercolor retrieval. This includes the sunglint reflection with water surface statistical distribution characteristics caused by imaging viewing angles differences, with high spatial resolution surface discrete characteristics sharing similar viewing angles, and the surface Fresnel reflection sunglint differences caused by the skylight difference during the flight of unmanned aerial vehicles. Aiming at the multiscale optical characteristics of sunglint reflection in high spatial resolution AOI, based on multi-path optical radiation transmission, the sunglint reflection interference from three different imaging processes is clarified. We developed a correction method to eliminate these different sunglint reflections on water surfaces and improve the reflectivity accuracy. The comparison with the in situ measured remote sensing reflectance of water indicated that the root mean square error (RMSE) was reduced from 0.0009 sr-1 to 0.0004 sr-1, and the mean relative error (MRE) decreased from 21.8% to 15.7%. This method has also been applied to correct the Airborne Visible Infrared Imaging Spectrometer (AVIRIS) images, showing good applicability. The method is fast, effective, and without auxiliary parameters, which provides a correction reference for different surface sunglint corrections of various AOI.

The Nord Stream pipeline gas leaks released approximately 220,000 tonnes of methane into the atmosphere.

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.

A global 0.05° dataset for gross primary production of sunlit and shaded vegetation canopies from 1992 to 2020.

Distinguishing gross primary production of sunlit and shaded leaves (GPPsun and GPPshade) is crucial for improving our understanding of the underlying mechanisms regulating long-term GPP variations. Here we produce a global 0.05°, 8-day dataset for GPP, GPPshade and GPPsun over 1992-2020 using an updated two-leaf light use efficiency model (TL-LUE), which is driven by the GLOBMAP leaf area index, CRUJRA meteorology, and ESA-CCI land cover. Our products estimate the mean annual totals of global GPP, GPPsun, and GPPshade over 1992-2020 at 125.0 ± 3.8 (mean ± std) Pg C a-1, 50.5 ± 1.2 Pg C a-1, and 74.5 ± 2.6 Pg C a-1, respectively, in which EBF (evergreen broadleaf forest) and CRO (crops) contribute more than half of the totals. They show clear increasing trends over time, in which the trend of GPP (also GPPsun and GPPshade) for CRO is distinctively greatest, and that for DBF (deciduous broadleaf forest) is relatively large and GPPshade overwhelmingly outweighs GPPsun. This new dataset advances our in-depth understanding of large-scale carbon cycle processes and dynamics.

Improved estimation of global gross primary productivity during 1981-2020 using the optimized P model.

Accurate estimation of terrestrial gross primary productivity (GPP) is essential for quantifying the net carbon exchange between the atmosphere and biosphere. Light use efficiency (LUE) models are widely used to estimate GPP at different spatial scales. However, difficulties in proper determination of maximum LUE (LUEmax) and downregulation of LUEmax into actual LUE result in uncertainties in GPP estimated by LUE models. The recently developed P model, as a LUE-like model, captures the deep mechanism of photosynthesis and simplifies parameterization. Site level studies have proved the outperformance of P model over LUE models. However, the global application of the P model is still lacking. Thus, the effectiveness of 5 water stress factors integrated into the P model was compared. The optimal P model was used to generate a new long-term (1981-2020) global monthly GPP dataset at a spatial resolution of 0.1° × 0.1°, called PGPP. Validation at globally distributed 109 FLUXNET sites indicated that PGPP is better than three widely-used GPP products. R2 between PGPP and observed GPP equals to 0.75, the corresponding root mean squared error (RMSE) and mean absolute error (MAE) equal to 1.77 g C m-2 d-1 and 1.28 g C m-2 d-1. During the period from 1981 to 2020, PGPP significantly increased in 69.02% of global vegetated regions (p < 0.05). Overall, PGPP provides a new GPP product choice for global ecology studies and the comparison of various water stress factors provides a new idea for the improvement of GPP model in the future.

Revisiting the cumulative effects of drought on global gross primary productivity based on new long-term series data (1982-2018).

Drought has broad and deep impacts on vegetation. Studies on the effects of drought on vegetation have been conducted over years. Recently, the cumulative effect of drought is recognized as another key factor affecting plant growth. However, global-scale studies on this phenomenon are still lacking. Thus, based on new satellite based gross primary productivity (GPP) and multi-temporal scale Standardized Precipitation Evapotranspiration Index data sets, we explored the cumulative effect duration (CED) of drought on global vegetation GPP and analyzed its variability across elevations and climatic zones. The main findings were as follows: (1) The cumulative effect of drought on GPP was widespread, with an average CED of 4.89 months. (2) CED of drought on GPP varied among vegetation types. Specifically, grasslands showed the longest duration, with an average value of 5.28 months, followed by shrublands (5.09 months), wetlands (5.03 months), croplands (4.85 months), savannas (4.58 months), and forestlands (4.57 months). (3) CED of drought on GPP changes with climate conditions. It decreased with the decrease of precipitation in the driest month (Pdry ) and mean annual precipitation in tropical and arid climate zones, respectively. In both temperate and cold climate zones, CED of drought on GPP was shorter in areas with dry winter than that in areas with dry summer. It increased with the decrease of mean annual air temperature in tropical climate zones and decreased with the increase of summer temperature in temperate and cold climatic zones. (4) With increasing elevation, CED of drought on GPP showed a pattern of increasing (0-3000 m), then decreasing (3000-5000 m), and increasing again (>5000 m). Our findings highlight the heterogeneity of CED of drought on GPP, owing to differences in vegetation types, long-term hydrothermal conditions, elevation, etc. The results could deepen our understanding of the effects of drought on global vegetation.

Anthropogenic emissions estimated using surface observations and their impacts on PM2.5 source apportionment over the Yangtze River Delta, China.

Source-tagged source apportionment (SA) has advantages for quantifying the contribution of various source regions and categories to PM2.5; however, it is highly affected by uncertainty in the emission inventory. In this study, we used a Regional multi-Air Pollutant Assimilation System (RAPAS) to optimize daily SO2, NOx and primary PM2.5 (PPM2.5) emissions in the Yangtze River Delta (YRD) in December 2016 by assimilating hourly in-situ measurements. The CMAQ-ISAM model was implemented with prior and posterior emissions respectively to investigate the impacts of optimizing emissions on PM2.5 SA in the YRD megalopolis (YRDM) and three megacities of Shanghai, Nanjing, and Hangzhou in the YRDM. The results showed that RAPAS significantly improved the simulations and reduced the emission uncertainties of the different pollutants. Compared with prior emissions, the posterior emissions in the YRD decreased by 13% and 11% for SO2 and NOx respectively, and increased by 24% for PPM2.5. Compared with SA using prior emissions, the contributions from Hangzhou, northern Zhejiang, and areas outside of the YRD to the YRDM increased. The local contributions from the YRDM, Nanjing and Shanghai decreased by 1.8%, 9.7%, and 2.3%, respectively, whereas that of Hangzhou increased by 5.6%. The changes in the daily local contributions caused by optimizing emissions ranged from -18.0% to 23.6%. Generally, under stable weather conditions, the local contribution changed the most, whereas under unstable weather conditions, the contribution from upwind areas changed significantly. Overall, with optimized emissions, we found in Nanjing, Shanghai, and Hangzhou, local emissions contributed 18.2%, 39.6% and 36.8% of their PM2.5 concentrations, respectively; long-range transport from outside the YRDM contributed 59.2%, 48.1%, and 48.2%, respectively. This study emphasizes the importance of improving emission estimations for source-tagged SA and provides more reliable SA results for the main cities in the YRD, which will contribute to pollution control in these regions.

The status of carbon neutrality of the world's top 5 CO2 emitters as seen by carbon satellites.

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.

Response to Comments on "Recent global decline of CO2 fertilization effects on vegetation photosynthesis".

Our study suggests that the global CO2 fertilization effect (CFE) on vegetation photosynthesis has declined during the past four decades. The Comments suggest that the temporal inconsistency in AVHRR data and the attribution method undermine the results' robustness. Here, we provide additional evidence that these arguments did not affect our finding and that the global decline in CFE is robust.

The effect of water stress on net primary productivity in northwest China.

Net primary productivity (NPP) has been widely used as the indicator of vegetation function and exhibits large spatial and temporal variations caused by numerous factors. Northwest China (NWC) is one of the driest regions in China, and water supply is the key determinant of NPP here. However, studies on the effects of water stress on NPP in NWC at the regional scale are still relatively lacking. Thus, in this study, based on a set of Moderate-Resolution Imaging Spectroradiometer (MODIS) NPP and evapotranspiration (ET) datasets, we quantified the response of NPP to water stress, which is indicated by crop water stress index (CWSI). Regional average of annual NPP in NWC showed an increasing trend during the study period, at a rate of 0.84 g C m-2 yr-1. At the province level, the NPP increase rates increased in the order of Ningxia (7.7%), Shaanxi (6.5%), Gansu (4.5%), Qinghai (3.8%), and Xinjiang (1.7%). NPP was negatively correlated with CWSI (p<0.05) in 73% of areas, indicating the key role of water stress in constraining NPP over this arid region. The effect of water stress on NPP changes with elevation. Water stress has the strongest negative impact on NPP in areas with elevations around 2000 m. In elevations above 5000 m, NPP is not limited by water stress, mostly positively correlated with CWSI. Our findings further clarify the importance of water stress in dryland ecosystems, while highlighting that elevation gradients can significantly affect the correlation between NPP and water stress.

Global positive gross primary productivity extremes and climate contributions during 1982-2016.

Gross primary production (GPP) quantifies the photosynthetic uptake of carbon by the terrestrial ecosystem. Positive GPP extremes represent the potential capacity of the terrestrial ecosystem to uptake carbon dioxide. Studying the positive GPP extreme is vital for the global carbon cycle and mitigation of global warming. With increasing climate extreme events, many kinds of research focus on studying negative GPP and the negative impact of climatic extremes on GPP. There is still a lack of research on positive GPP extremes and whether climatic extremes could be beneficial to global carbon uptake. In this study, we used daily Boreal Ecosystem Productivity Simulator (BEPS) to simulate GPP of the global terrestrial ecosystem during 1982-2016 and combined TRENDY models to detect positive GPP extremes and investigate the effects of climate extremes on GPP. We found the results of the TRENDY models have large differences in some areas of the globe, and the BEPS model driven by remote sensing data could be more suitable for simulating the long-term time series of global terrestrial GPP. Compared to other plant functional types, grasslands contributed the most to positive GPP extremes, accounting for approximately 41.6% (TRENDY) and 34.8% (BEPS) of the global positive GPP extremes. The probabilities of positive GPP extremes caused by positive precipitation extremes were significantly higher than those caused by temperature and radiation in most areas of the globe, indicating that sufficient precipitation (not a flood) would boost the carbon uptake ability of the global terrestrial ecosystem to form positive GPP extremes. On the contrary, the partial correlation coefficients between temperature and GPP were negative in most areas of globe, suggesting that global warming will not be conducive to carbon uptake of the terrestrial ecosystem. This study may provide new knowledge on the global positive GPP extremes.

Tracking the seasonal and inter-annual variations of global gross primary production during last four decades using satellite near-infrared reflectance data.

Terrestrial vegetation absorbs approximately 30% of the anthropogenic carbon dioxide (CO2) emitted into the atmosphere through photosynthesis (represented by gross primary productivity, GPP) and thus effectively mitigates global warming. However, large uncertainties still remain in the global GPP estimations and their long-term trends. Here we used the satellite-based near-infrared reflectance (NIRv) as the proxy of GPP and generated a global long-term (1982-2018) GPP datasets (hereafter GPPNIRv). Analysis at the site-level showed that NIRv could accurately capture both the monthly and annual variations in GPP (R2 = 0.71 and 0.74 respectively) at 104 flux sites. Upscaling the relationships between NIRv and GPP to the global scale, the global annual GPP was estimated to be 128.3 ± 4.0 Pg C yr-1 during the last four decades, which fell between the estimations from the machine-learning upscaling approach, light-use-efficiency (LUE) models and processed-based models. The seasonal variation of GPPNIRv was also consistent with those from flux sites and models. More importantly, the inter-annual trends in GPPNIRv during the last four decades were consistent with those from processed-based models across latitudes, which outperformed the other GPP products. This evidence suggested that the long-term GPP datasets derived from NIRv have better abilities to capture the seasonal and inter-annual variations of terrestrial GPP at the global scale. The long-term GPPNIRv product could be beneficial for the estimation of terrestrial carbon fluxes and for the projection of future climates.

Recent global decline of CO2 fertilization effects on vegetation photosynthesis.

The enhanced vegetation productivity driven by increased concentrations of carbon dioxide (CO2) [i.e., the CO2 fertilization effect (CFE)] sustains an important negative feedback on climate warming, but the temporal dynamics of CFE remain unclear. Using multiple long-term satellite- and ground-based datasets, we showed that global CFE has declined across most terrestrial regions of the globe from 1982 to 2015, correlating well with changing nutrient concentrations and availability of soil water. Current carbon cycle models also demonstrate a declining CFE trend, albeit one substantially weaker than that from the global observations. This declining trend in the forcing of terrestrial carbon sinks by increasing amounts of atmospheric CO2 implies a weakening negative feedback on the climatic system and increased societal dependence on future strategies to mitigate climate warming.

NO x Emission Changes Over China During the COVID-19 Epidemic Inferred From Surface NO2 Observations.

The COVID-19 epidemic has substantially limited human activities and affected anthropogenic emissions. In this work, daily NO x emissions are inferred using a regional data assimilation system and hourly surface NO2 measurement over China. The results show that because of the coronavirus outbreak, NO x emissions across the whole mainland China dropped sharply after 31 January, began to rise slightly in certain areas after 10 February, and gradually recover across the country after 20 February. Compared with the emissions before the outbreak, NO x emissions fell by more than 60% and ~30% in many large cities and most small to medium cities, respectively. Overall, NO x emissions were reduced by 36% over China, which were mainly contributed by transportation. Evaluations show that the inverted changes over eastern China are credible, whereas those in western China might be underestimated. These findings are of great significance for exploring the reduction potential of NO x emissions in China.