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Feeding an increasingly affluent population is a huge challenge facing global agriculture. In contrast to “large-scale farming” in developed economies (e.g. Europe and the United States), developing countries are dominated by “smallholder farms” relying on traditional farming practices but increasingly with substantial nitrogen overuse leading to severe environmental degradation and adverse human health. Here, we explore the potential for better nitrogen use by synthesizing the global relationship between farm size and nitrogen use for 16 major crops, assess the impact of farm size on nitrogen flows, and link these with air quality modelling to produce an integrated assessment of nitrogen-related environmental and health outcomes related to farm size. We find that increasing farm size in developing countries can contribute to more efficient and sustainable farming practices, which could decrease nitrogen overuse, ammonia emissions and nitrogen deposition by 20-25%, increase nitrogen use efficiency by 2-8%, and save over 142,000 premature deaths per year related to PM2.5 air pollution. Although a large one-time investment is required for increasing farm size, there would be substantial progress towards achieving Sustainable Development Goals, associated with food security, a clean environment and improved human health.

Growing population and consumption pose unprecedented demands on food production. However, ammonia emissions mainly from food systems increase oceanic nitrogen deposition contributing to eutrophication. Here, we developed a long-term oceanic nitrogen deposition dataset (1970 to 2018) with updated ammonia emissions from food systems, evaluated the impact of ammonia emissions on oceanic nitrogen deposition patterns, and discussed the potential impact of nitrogen fertilizer overuse. Based on the chemical transport modeling approach, oceanic ammonia-related nitrogen deposition increased by 89% globally between 1970 and 2018, and now, it exceeds oxidized nitrogen deposition by over 20% in coastal regions including China Sea, India Coastal, and Northeastern Atlantic Shelves. Approximately 38% of agricultural nitrogen fertilizer was excessive, which corresponds to 15% of global oceanic ammonia-related nitrogen deposition. Policymakers and water quality managers need to pay increasingly more attention to ammonia associated with food production if the goal of reducing coastal nitrogen pollution is to be achieved for Sustainable Development Goals.

The direct energy balance control strategy is analyzed and combined with the characteristics of supercritical units. The direct energy balance strategy is reconstructed. The reconstructed direct energy balance signal is applied to the boiler master control feedforward in the coordinated control, taking a power plant’s coordinated control system as an example, t. Thus, it is applied to the control of supercritical units, which improves the regulation quality of the control system in a steady and dynamic state, and effectively solves the problems of large fluctuation of mainstream pressure, locked increase, and decrease of load command, frequent withdrawal of coordinated control and unqualified AGC index.

Significance Agricultural systems are already major forces of ammonia pollution and environmental degradation. How agricultural ammonia emissions affect the spatio-temporal patterns of nitrogen deposition and where to target future mitigation efforts, remains poorly understood. We develop a substantially complete and coherent agricultural ammonia emissions dataset in nearly recent four decades, and evaluate the relative role of reduced nitrogen in total nitrogen deposition in a spatially explicit way. Global reduced nitrogen deposition has grown rapidly, and will occupy a greater dominant position in total nitrogen deposition without future ammonia regulations. Recognition of agricultural ammonia emissions on nitrogen deposition is critical to formulate effective policies to address ammonia related environmental challenges and protect ecosystems from excessive nitrogen inputs.

Our study suggests that the global CO 2 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.

Background Vegetation phenology research has largely focused on temperate deciduous forests, thus limiting our understanding of the response of evergreen vegetation to climate change in tropical and subtropical regions. Results Using satellite solar-induced chlorophyll fluorescence (SIF) and MODIS enhanced vegetation index (EVI) data, we applied two methods to evaluate temporal and spatial patterns of the end of the growing season (EGS) in subtropical vegetation in China, and analyze the dependence of EGS on preseason maximum and minimum temperatures as well as cumulative precipitation. Our results indicated that the averaged EGS derived from the SIF and EVI based on the two methods (dynamic threshold method and derivative method) was later than that derived from gross primary productivity (GPP) based on the eddy covariance technique, and the time-lag for EGSsif and EGSevi was approximately 2 weeks and 4 weeks, respectively. We found that EGS was positively correlated with preseason minimum temperature and cumulative precipitation (accounting for more than 73% and 62% of the study areas, respectively), but negatively correlated with preseason maximum temperature (accounting for more than 59% of the study areas). In addition, EGS was more sensitive to the changes in the preseason minimum temperature than to other climatic factors, and an increase in the preseason minimum temperature significantly delayed the EGS in evergreen forests, shrub and grassland. Conclusions Our results indicated that the SIF outperformed traditional vegetation indices in capturing the autumn photosynthetic phenology of evergreen forest in the subtropical region of China. We found that minimum temperature plays a significant role in determining autumn photosynthetic phenology in the study region. These findings contribute to improving our understanding of the response of the EGS to climate change in subtropical vegetation of China, and provide a new perspective for accurately evaluating the role played by evergreen vegetation in the regional carbon budget.

Photosynthetic phenology has large effects on the land–atmosphere carbon exchange. Due to limited experimental assessments, a comprehensive understanding of the variations of photosynthetic phenology under future climate and its associated controlling factors is still missing, despite its high sensitivities to climate. Here, we develop an approach that uses cities as natural laboratories, since plants in urban areas are often exposed to higher temperatures and carbon dioxide (CO2) concentrations, which reflect expected future environmental conditions. Using more than 880 urban–rural gradients across the Northern Hemisphere (≥30° N), combined with concurrent satellite retrievals of Sun-induced chlorophyll fluorescence (SIF) and atmospheric CO2, we investigated the combined impacts of elevated CO2 and temperature on photosynthetic phenology at the large scale. The results showed that, under urban conditions of elevated CO2 and temperature, vegetation photosynthetic activity began earlier (−5.6 ± 0.7 d), peaked earlier (−4.9 ± 0.9 d) and ended later (4.6 ± 0.8 d) than in neighbouring rural areas, with a striking two- to fourfold higher climate sensitivity than greenness phenology. The earlier start and peak of season were sensitive to both the enhancements of CO2 and temperature, whereas the delayed end of season was mainly attributed to CO2 enrichments. We used these sensitivities to project phenology shifts under four Representative Concentration Pathway climate scenarios, predicting that vegetation will have prolonged photosynthetic seasons in the coming two decades. This observation-driven study indicates that realistic urban environments, together with SIF observations, provide a promising method for studying vegetation physiology under future climate change. Cities can be used as natural laboratories for higher temperatures and CO2 concentrations. Urban vegetation photosynthetic activity begins earlier, peaks earlier and ends later than in neighbouring rural areas.

Nitrogen (N) deposition changes the availability of N in ecosystems and regulates carbon and water cycles, including ecosystem water use efficiency (WUE). In recent decades, N deposition has been increasing rapidly, especially at several hotspots in China, Europe and the U.S. In this study, a process-based terrestrial ecosystem model, IBIS (Integrated BIosphere Simulator), was used to simulate the effects of N deposition on WUE globally. The results indicated that increasing N deposition led to a global increase in WUE of 0.005 g C kg−1 H2O on average over the first 10 years of the 21st century. The effects of N deposition on WUE were more evident in certain hotspots than in other parts of the world. The effect of N deposition on gross primary productivity (GPP) determined the change in WUE. In particular, high N deposition led to decreased ET and consequently increased the WUE in southeastern China. Moreover, increased N deposition has significantly enhanced the WUE in response to elevated CO2 in southeastern China, indicating the importance of N deposition in regulating the terrestrial carbon cycle.

Remotely sensed solar-induced fluorescence (SIF) has emerged as a novel and powerful approach for terrestrial vegetation monitoring. Continuous measurements of SIF in synergy with concurrent eddy c...

Future projections of climate extremes are expected to become more frequent. Parts of Europe experienced an extensive heatwave and drought during 2018. However, its impacts on terrestrial carbon cycle remain elusive. Here we investigated the vegetation responses to the heatwave and drought during 2018 based on satellite solar-induced chlorophyll fluorescence (SIF) and near-infrared reflectance (NIRv) data, which were used to estimate gross primary productivity (GPP). Results showed that there were no significant (p= 0.60) reductions in GPP across most of Europe during April-August of 2018. The higher temperatures in spring enhanced vegetation GPP, largely alleviated the negative impacts of heatwave and drought on vegetation photosynthesis during the subsequent summer, which resulted in evident compensation effects. Concurrently, warmer spring also had lagged effects by diminishing soil moisture, accompanied by scarce precipitation, leading to water stress on plant growth during summer. This observation-based study highlights the need for more considerations of seasonal compensation and lagged effects on the interactions between climate extreme events and biosphere.

In recent years, a large number of electrochemical energy storage projects have gradually emerged and capability of battery is also becoming central issue for research. The battery control strategy directly affects the response to AGC (Automatic Generation Control). If the control is improper, it is easy to cause the remaining power of each battery cluster unbalanced and influence the battery life. This article mainly focuses on the research on the distribution of power commands from the battery management system, and proposes a power distribution method. The method was put into practice in an energy storage project which got good results.

The terrestrial carbon cycle is an important part of the global carbon budget due to its large gross exchange fluxes with the atmosphere and their sensitivity to climate change. Terrestrial biosphere models show large uncertainties in estimating carbon fluxes, which impacts global carbon budget assessments. The land surface carbon cycle is tightly controlled by soil moisture through plant physiological processes. In this context, accurate soil moisture data will improve the modeling of carbon fluxes in a model-data fusion framework. We employ the Carbon Cycle Data Assimilation System (CCDAS) to assimilate 36 years (1980-2015) of surface soil moisture data as provided by the ESA CCI in combination with atmospheric CO2 concentration observations at global scale. We will present the methods used for assimilating long-term remotely sensed soil moisture into the terrestrial biosphere model, and demonstrate the importance of soil moisture in modeling ecosystem carbon cycle processes. We will also investigate the impacts of soil moisture on the terrestrial carbon cycle during climate extremes at various scales.

Remotely sensed solar-induced fluorescence (SIF) has emerged as a novel approach for terrestrial vegetation monitoring. The in situ continuous optical remote sensing tool in conjunction with concur...

A decline in the carbon fertilization effect One source of uncertainty in climate science is how the carbon fertilization effect (CFE) will contribute to mitigation of anthropogenic climate change. Wang et al. explored the temporal dynamics of CFE on vegetation photosynthesis at the global scale. There has been a decline over recent decades in the contribution of CFE to vegetation photosynthesis, perhaps owing to the limiting effects of plant nutrients such as nitrogen and phosphorus. This declining trend has not been adequately accounted for in carbon cycle models. CFE thus has limitations for long-term mitigation of climate change, and future warming might currently be underestimated. Science , this issue p. 1295

Photosynthetic phenology has large effects on the land-atmosphere carbon exchange. Due to limited experimental assessments, a comprehensive understanding of the variations of photosynthetic phenology under future climate and its associated controlling factors is still missing, despite its high sensitivities to climate. Here, we develop an approach that uses cities as natural laboratories, since plants in urban areas are often exposed to higher temperatures and carbon dioxide (CO

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.

Satellite remotely sensed fraction of photosynthetically active radiation (FPAR) products are widely used in land-surface monitoring and modeling, especially for estimating global terrestrial photosynthetic activity through light use efficiency (LUE) models. PAR absorbed by active chlorophyll (APARchl) is directly linked to vegetation photosynthesis and can be used to estimate ecosystem gross primary production (GPP). Previous studies have demonstrated that solar induced chlorophyll fluorescence has very tight relationship with APARchl at various ecosystems. Therefore, the solar angle normalized SIF (nSIF) is directly related to the fraction of PAR absorbed by chlorophyll (FPARchl). This paper intercompared six space FPAR products from Moderate Resolution Imaging Spectroradiometer (MODIS), Visible Infrared Imaging Radiometer Suite (VIIRS), Copernicus Global Land Service (CGLS), Multi-angle Imaging SpectroRadiometer (MISR), Earth Polychromatic Imaging Camera (EPIC) and Ocean and Land Colour Instrument (OLCI). Their potential relationships with FPARchl were indirectly evaluated with both spaceborne (Orbiting Carbon Observatory-2, OCO-2 and TROPOspheric Monitoring Instrument, TROPOMI) and airborne (Chlorophyll Fluorescence Imaging Spectrometer, CFIS) nSIF data as well as in situ GPP measurements. Our results show that these FPAR products are different in terms of amplitudes and seasonal variations across biomes. Among six FPAR products, OLCI FPAR shows the best relationships with TROPOMI nSIF740, OCO-2 nSIF757, and CFIS nSIF755. The coefficient of determination (R2) for the relationship between OLCI FPAR and TROPOMI nSIF740 is 0.79 ± 0.17 on a global average. APAR calculated from OLCI also exhibits the best relationship (R2 = 0.79) with in situ GPP over 25 flux towers.

Remote sensing of solar-induced chlorophyll fluorescence (SIF) provides great potential for estimating gross primary production (GPP) of terrestrial ecosystems. A strong relationship between SIF and GPP has been observed at the seasonal scale from both ground-based and satellite observations. However, variations of SIF due to changes in plant growth stages appear to influence the SIF-GPP relationship. It remains unclear how this relationship is affected by plant growth-related changes, especially for C4 plants such as maize. In this study, continuous in situ measurements for canopy far-red SIF retrieval and GPP calculation were made in maize during the growing season of 2017. Diurnal and seasonal variations of canopy SIF and its yield (SIFyield) were analyzed over different growth stages of maize to understand how they affect the relationship with GPP. The results show that the relationship between SIF and GPP varies with the growth stages of maize during the growing season, indicating that canopy structure has a strong impact on the seasonal variations of canopy SIF and its relation to GPP. Furthermore, we found that SIFyield is significantly correlated with canopy photosynthetic light use efficiency (LUE) at the canopy level throughout the season. However, it is almost uncorrelated with LUE after adjusting for the effects of canopy structure with the structural vegetation index MTVI2. This finding highlights the importance of canopy structure in the relationship between SIFyield and LUE, complicating the use of canopy SIF for tracking vegetation physiological activity. Overall, our observation-based findings show that canopy structure affects the SIF-GPP relationship, strengthening our understanding of the mechanistic link between SIF and photosynthesis.

On-board the Landsat-8 satellite, the Thermal Infrared Sensor (TIRS), which has two adjacent thermal channels centered roughly at 10.9 and 12.0 μm, has a great benefit for the land surface temperature (LST) retrieval. The single-channel algorithm (SC) and split-window algorithm (SW) have been applied to retrieve the LST from TIRS data, which need the land surface emissivity (LSE) as prior knowledge. Due to the big challenge of determining the LSE, this study develops a temperature and emissivity separation algorithm which can simultaneously retrieve the LST and LSE. Based on the laboratory emissivity spectrum data, the minimum-maximum emissivity difference module (MMD module) for TIRS data is developed. Then, an emissivity log difference method (ELD method) is developed to maintain the emissivity spectrum shape in the iterative process, which is based on the modified Wien’s approximation. Simulation results show that the root-mean-square-errors (RMSEs) are below 0.7 K for the LST and below 0.015 for the LSE. Based on the SURFRAD ground measurements, further evaluation demonstrates that the average absolute error of the LST is about 1.7 K, which indicated that the algorithm is capable of retrieving the LST and LSE simultaneously from TIRS data with fairly good results.

Chinese Academy of Sciences, Nanjing Institute of Geography and Limnology,No. 73 East Beijing Road, Nanjing 210008, ChinaAbstract. Apracticalsplit-windowalgorithmwhichinvolvestwoparameters(transmittanceandemissivity) utilized to retrieve land-surface temperature over agricultural areas from theAdvanced Spaceborne Thermal Emission and Reflection Radiometer data is presented. First,by calculating the relationship between thermal radiation intensity and temperature, thePlanck function is simplified using exponential function which is applied to deduce thesplit-window algorithm. Second, how to obtain transmittance from water vapor content andthe method for estimating emissivity using normalized difference vegetation index are discussedin detail. Sensitivity analysis demonstrates that the algorithm is not sensitiveto these twoparam-eters. Finally, a standard atmospheric simulation method has been used to validate the proposedalgorithm, and comparison between the algorithm and the prior study has been carried out. Theresults indicate that the average accuracy is 0.32 K for the case without error in both transmit-tance and emissivity, which is better than the prior algorithm. The accuracy is also 0.32 K whenthe transmittance is computed from the water content by piecewise cubic polynomial fit. Theaccuracy is about 0.30 K ∼0.33 K corresponding to different Pv (Pv is the proportion of veg-etation)values,whichindicatesthatthisalgorithmissuitablefordifferentlandsurfacetypesoveragricultural areas.