Net Primary Productivity Data Research Articles

Time series of phytoplankton net primary production reveals intense interannual variability and size‐dependent chlorophyll‐specific productivity on a continental shelf

AbstractPhytoplankton community size structure influences the production and fate of organic carbon in marine food webs and can undergo strong seasonal shifts in temperate regions. As part of the Northeast US Shelf (NES) Long‐Term Ecological Research program, we measured net primary production (NPP) rates and chlorophyll a (Chl a) concentrations in three phytoplankton size classes (< 5, 5–20, and > 20 μm) during winter and summer for 3 yr along a coastal‐to‐offshore transect. Mean depth‐integrated NPP was 37% higher in summer than winter, with limited cross‐shelf differences because of significant interannual variability. When averaged across the shelf, depth‐integrated NPP was dominated by the > 20 μm size class in winter and generated equally by the three size fractions in summer because of substantial contributions from cells > 20 μm at the Chl a maximum depth. Furthermore, the relationship between Chl a and NPP, in terms of relative contributions, varied by size class. Variations in this relationship have implications for models of primary productivity on the NES and beyond. In comparison to historical NPP data, we identified equivalent levels of winter NPP but observed a 25% decrease in summer NPP, suggesting a potential reduction in the seasonality of NPP on the NES. Together, our results highlight seasonal shifts in NPP rates of different phytoplankton size classes, with implications for food web structure and export production. These data emphasize the importance of quantifying size‐fractionated NPP over time to constrain its variability and better predict the fate of organic carbon in coastal systems under environmental change.

Modeling carbon dynamics from a heterogeneous watershed in the mid-Atlantic USA: A distributed-calibration and independent verification (DCIV) approach

The terrestrial ecosystem plays a vital role in regulating regional and global carbon budgets. Ecosystem models are extensively employed to estimate carbon fluxes across different spatial scales. However, there remains a need to reduce the uncertainties associated with model parameterization and input data. To address these limitations, we assessed a distributed-calibration and independent-verification (DCIV) approach that uses (1) remotely sensed net primary production (NPP) and evapotranspiration (ET) data from the Moderate Resolution Imaging Spectroradiometer (MODIS), (2) multi-site eddy covariance net ecosystem exchange (NEE) data; and (3) field sampling of soil organic carbon (SOC) and aboveground biomass (ABG) data to improve the overall predictability of carbon fluxes for the different land use and land cover (LULC) types at a watershed scale. The DCIV approach was applied to an advanced version of the Soil and Water Assessment Tool (SWAT)-Carbon (or SWAT-C), equipped with Century-based SOC algorithms to simulate carbon dynamics for watersheds with heterogeneous vegetation. The objective of the modeling effort was to assess carbon stocks and fluxes under different land management scenarios for a 3000-acre experimental farm and forest preserve in the northeastern United States. Our study showed that a large SOC stock of at least 100 tons ha−1 is stored under mixed forest, deciduous, shrubland, and floodplain (grass). Our study also showed that converting floodplain (grass) to deciduous forest has the potential to increase CO2 uptake (-NEE) by an order of three magnitude and ABG by 77 %, leading to an increased SOC stock of 23 % after twenty years. Similarly, we found that converting ungrazed grassland to grazed pasture leads to a non-statistically decreasing trend of SOC, especially in the 0–30 cm soil layer. Thus, the methodology used in this study can be applied to improve carbon dynamic prediction from a heterogeneous watershed at a regional scale.

Using a Light Gradient-Boosting Machine–Shapley Additive Explanations Model to Evaluate the Correlation Between Urban Blue–Green Space Landscape Spatial Patterns and Carbon Sequestration

Global ecosystems are facing challenges posed by warming and excessive carbon emissions. Urban areas significantly contribute to carbon emissions, highlighting the urgent need to improve their ability to sequester carbon. While prior studies have primarily examined the carbon sequestration benefits of single green or blue spaces, the combined impact of urban blue–green spaces (UBGSs) on carbon sequestration remains underexplored. Meanwhile, the rise of machine learning provides new possibilities for assessing this nonlinear relationship. We conducted a study in the Yangzhou urban area, collecting Landsat remote sensing data and net primary productivity (NPP) data at five-year intervals from 2001 to 2021. We applied the LightGBM-SHAP model to systematically analyze the correlation between UBGSs and NPP, extracting key landscape metrics. The results indicated that landscape metrics had varying impacts on NPP. At the patch and type level, the Percentage of Landscape was significantly positively correlated with NPP in green space, while the contiguity index and fractal dimension index favored carbon sequestration under certain conditions. The contribution of blue space was lower, with some indicators exhibiting negative correlations. At the landscape level, the contagion index and aggregation index of UBGS had positive effects on NPP, while the division index and landscape shape index were negatively correlated with NPP. The results enhance the understanding of the relationship between UBGS and carbon sequestration, and provide a reference for urban planning.

Assessment of global carbon dynamics due to mining-induced forest cover loss during 2000–2019 using satellite datasets

Mining activities significantly contribute to forest cover loss (FCL), subsequently altering global carbon dynamics and exacerbating climate change. The present study aims to estimate the contributions of mining-induced FCL to carbon sequestration loss (CSL) and carbon dioxide (CO₂) emissions from 2000 to 2019 using the proxy datasets. For FCL analysis, the global FCL data at 30 m spatial resolution, developed by Hansen et al. (2013), was employed in the Google Earth Engine (GEE) cloud platform. Furthermore, for CSL and CO₂ emissions assessment, Moderate Resolution Imaging Spectroradiometer (MODIS)-based Net Primary Productivity (NPP) data and Zhang and Liang (2020)-developed biomass datasets were used, respectively. The outcomes of the study exhibited approximately 16,785.90 km2 FCL globally due to mining activities, resulting in an estimated CSL of ∼36,363.17 Gg CO₂/year and CO₂ emissions of ∼490,525.30 Gg CO₂. Indonesia emerged as the largest contributor to mining-induced FCL, accounting for 3,622.78 km2 of deforestation, or 21.58% of the global total. Brazil and Canada followed, with significant deforestation and CO₂ emissions. The findings revealed that mining activities are a major driver of deforestation, particularly in resource-rich regions, leading to substantial environmental degradation. The relative FCL was notably high in smaller countries like Suriname and Guyana, where mining activities constituted a significant proportion of total deforestation. The present study underscores the urgent need for robust regulatory frameworks, sustainable land management practices, and coordinated international efforts to mitigate the adverse environmental impacts of mining. The findings of this study can inform policymakers and stakeholders, leading to more effective conservation strategies and benefiting society by promoting environmental sustainability and resilience against climate change.

Spatial Characteristics and Driving Factors of Net Primary Productivity of Vegetation in the Upper and Middle Yellow River Basin

The Net Primary Productivity （NPP） of vegetation plays a crucial role in terrestrial ecosystems, and a detailed investigation into the annual average NPP and its driving factors is of significant importance for promoting regional ecological construction and sustainable development. This research utilized MOD17A3 annual average NPP data from 2000 to 2020 and employed methods such as trend analysis, Hurst index, random forest model, partial dependence model, geographic weighted regression, and partial least squares-structural equation model （PLS-SEM） to analyze the annual variation characteristics of NPP and its relationship with driving factors in the upper and middle reaches of the Yellow River. The results showed： ① During the period from 2000 to 2020, the annual average NPP in the upper and middle reaches of the Yellow River generally exhibited a year-on-year increasing trend, with 79.25% of the region showing a significant improvement in annual average NPP, while 0.25% of the region exhibited a severe degradation trend. ② The dominant influencing factors of the annual average NPP in the upper and middle reaches of the Yellow River included precipitation, NDVI, drought, and relative humidity. The suitable range for increasing annual average NPP was 400 mm to 650 mm for precipitation and 40% to 70% for relative humidity, and NDVI showed a significantly linear positive correlation with annual average NPP. Drought index was negatively correlated with annual average NPP. The annual average NPP tended to stabilize and did not decline further when the drought index exceeded the threshold of 8. ③ Extreme rainfall indirectly affected the variation in annual average NPP in the basin by influencing the vegetation growth condition. The impact degree of extreme rainfall indices on the annual average NPP in the basin was in the following order： R10>R95P>PRCPTOT>SDII>Rx5day>R99P. ④ The next steps in ecological construction in the upper and middle reaches of the Yellow River should focus on the arid and severely arid regions in the northern part of the Yellow River, adapting measures to local conditions and enhancing the regional ecological environment.

Multi-Scale Analysis of Carbon Emissions in Coastal Cities Based on Multi-Source Data: A Case Study of Qingdao, China

Coastal cities, as centers of economic and industrial activity, accommodate over 40% of the national population and generate more than 70% of the GDP. They are critical centers of carbon emissions, making the accurate and long-term analysis of spatiotemporal carbon emission patterns crucial for developing effective regional carbon reduction strategies. However, there is a scarcity of studies on continuous long-term carbon emissions in coastal cities. This study focuses on Qingdao and explores its carbon emission characteristics at the city, county, and grid scales. Data from multi-source are employed, integrating net primary production (NPP), energy consumption, and nighttime light data to construct a carbon emission estimation model. Additionally, the Tapio model is applied to examine the decoupling of GDP from carbon emissions. The results indicate that the R2 of the carbon emission inversion model is 0.948. The central urban areas of Qingdao’s coastal region are identified as hotspots for carbon emissions, exhibiting significantly higher emissions compared to inland areas. There is a notable dependence of economic development on carbon emissions, and the disparities in economic development between coastal and inland areas have resulted in significant geographical differentiation in the decoupling state. Furthermore, optimizing and transitioning the energy structure has primarily contributed to carbon reduction, while exceptional circumstances, such as the COVID-19 pandemic, have led to passive fluctuations in emissions. This study provides a scientific reference for coastal cities to formulate targeted carbon reduction policies.

An ecological framework to index crop yields using productivity and Ecosystem Fit: A case study from India

On the global scale, agricultural crop yields have decreased or plateaued over the last several decades. This suggests that the current focus on selecting crop varieties based on a plant’s light-use efficiency (photosynthetic and nitrogen-use-efficiency metrics) may not be sensitive to the site’s edaphic parameters, which limit growth. This study introduces a new framework to determine if crops can achieve higher yield potentials by assessing how plants adapt to the edaphic properties that impact growth, especially when contending with climate change. The new approach calculates an Ecosystem Fit index using a ratio of remotely sensed (or observed) total net primary productivity to the theoretical maximum productivity of the site. Then, it uses that index as a benchmark to judge quantitatively whether any new crop species or variety is improving potential biomass or economic yields at that specific site. It can also determine the best soil types for those crop varieties and monitor their potential adaptability relative to climate change over time. This study used a database of 356 spatially independent reference sites to develop this framework using a landcover classification of crops across 21 ecoregions and five biomes in India. It includes total net primary productivity data, theoretical maximum productivity potential, and soil and climatic data. This comparison showed that the light-use efficiency model, as intended, was not sensitive to variations in soil characteristics, temperature, or precipitation. Our framework showed significant differences in growth by soil type and precipitation and three significant productivity thresholds by soil type. The results of this study demonstrate that total crop productivity and Ecosystem Fit create a useful index for local land managers to assess growth and yield potentials across diverse edaphic landscapes and for decision-making with changing climates.

Does vegetation greening have a positive effect on global vegetation carbon and water use efficiency?

Terrestrial ecosystems have undergone significant changes as a result of climate change, profoundly affecting global carbon and water cycling processes. Notably, the synergistic changes in vegetation carbon use efficiency (CUE) and water use efficiency (WUE) and their response to patterns of climate change over the last 40 years are unknown. Therefore, in this study, global vegetation WUE and CUE were inverted using Gross primary productivity (GPP), Net primary productivity (NPP) and total evaporation (ET) data from 1981 to 2019 to reveal their temporal and spatial patterns of change through trend analysis and stability analysis. A stepwise regression algorithm was used to reveal the potential driving law of environmental factors on vegetation WUE and CUE. The results shows that (1) From 1981 to 2019, the global vegetation WUE and CUE showed in a relatively stable state, and the trends of WUE and CUE were -0.00004/year and 0.006 g C m−2 mm−1/year, respectively; (2) the greening of vegetation was the most important cause of the changes in WUE and CUE, and the driving force of rain and heat conditions on the CUE of vegetation was smaller than that of solar radiation and soil water, the regions where CO2 is the dominant factor affecting CUE and WUE are mainly in the north temperate zone; (3) the region of synergistic growth of WUE and CUE accounts for about 31.38 % of the global terrestrial area, and this pattern of change suggests that the global vegetation carbon sink potential is huge, and the popularization of vegetation planting patterns under the synergistic growth of CUE and WUE should be strengthened. The research has shown that vegetation greening is a key factor influencing changes in the WUE and CUE of vegetation, therefore, the implementation of ecological engineering will be an important step in combating climate change.

Spatiotemporal Variation Characteristics and Driving Factors of Vegetation NPP in the Ulansuhai Nur Basin from 2001 to 2020

Vegetation net primary productivity （NPP） represents the ability of plants to fix ecosystem carbon, which is a key indicator to determine the health status and sustainable development of ecosystems. Its spatial and temporal changes and driving factors play an important role in revealing the status of vegetation restoration and guiding ecological restoration. Based on MODIS17A3 NPP data, land use, and meteorological data from 2001 to 2020, the temporal and spatial variation characteristics and driving factors of vegetation NPP in the Ulansuhai Nur Basin of Inner Mongolia were explored by using the methods of coefficient of variation, Theil-Sen Median trend analysis, Mann-Kendall significance test, Hurst index, and Geodetector. The results showed that： ① From 2001 to 2020, the vegetation NPP in the Ulansuhai Nur Basin showed a fluctuating upward trend, with an average value （in terms of C） of 141.03 g·（m2·a）-1 and an average increase rate of 2.33 g·（m2·a）-1. The vegetation NPP had obvious spatial differentiation, which was characterized by high in the southwest and low in the northeast and high in Hetao Plain and low in sandy land and mountainous areas. ② NPP mainly showed an increasing trend, and the area proportions of increasing, decreasing, and unchanged areas were approximately 80%, 3%, and 17%, respectively. The average coefficient of variation of vegetation NPP was 0.149, which mainly showed low fluctuation change, and the area accounted for approximately 51%. The future change trend of NPP was mainly characterized by anti-persistence, with an area ratio of approximately 75%. ③ Land use, altitude, maximum temperature, and slope were the dominant driving factors of variation NPP change in the Ulansuhai Nur Basin, and the q values were all above 0.200. The interaction between altitude and relative humidity had the greatest explanatory power for the spatial differentiation of vegetation NPP in the Ulansuhai Nur Basin. There were significant differences in the explanatory power of land use and all factors except nighttime light to the spatial differentiation of vegetation NPP in the Ulansuhai Nur Basin. According to the research results, in the future, we should strengthen the ecosystem management of the Ulansuhai Nur Basin； continue to implement strict ecological protection and restoration policies； and comprehensively consider factors such as climate, topography, and human activities to carry out comprehensive ecological management according to local conditions to improve the quality of ecosystem services.

Evolution characteristics and meteorological impacts of ecosystem regulation service functions in Jiangxi Province, China from 2000 to 2022.

Jiangxi Province is one of the first ecological civilization demonstration provinces in China. Understan-ding the impacts of meteorological conditions on ecosystem regulatory services is beneficial for conducting ecological protection and restoration work. Based on MODIS data, net primary productivity data, and monthly meteorological data from 2000 to 2022, we used models such as water balance equation and soil loss equation to measure the four regulatory service functions of ecosystem in Jiangxi Province, including carbon sequestration, oxygen release, water conservation and soil conservation. We used trend analysis and partial correlation analysis methods to analyze the spatio-temporal patterns and meteorological influencing factors of those four regulation service functions. The results showed that from 2000 to 2022, the annual average values of carbon sequestration and oxygen release in Jiangxi Province were 178.8 and 130.0 g·m-2, respectively, with annual increases of 0.4 and 0.3 g·m-2. The spatial distribution of both services was consistent, and the average annual carbon sequestration and oxygen release showed an upward trend in 77.3% regions of Jiangxi Province. The average water conservation and soil retention in Jiangxi Province were 591.8 mm and 723.8 t·hm-2, respectively, with similar spatial distributions. The annual increases were 5.6 mm and 3.7 t·hm-2. The soil conservation and water conservation functions of 73.3% and 69.3% regions in Jiangxi Province were steadily improved. Vegetation carbon sequestration and oxygen release was significantly correlated with temperature at monthly scale and seasonal scale. The partial correlation coefficient of those two factors was higher than other factors, which was an important meteorological factor affecting the carbon sequestration and oxygen release function of ecosystem. Precipitation, which was the most important meteorological factor, had a significant positive correlation with water conservation and soil conservation at monthly, seasonal and annual scales. Our results revealed the impacts of climate change on ecosystem regulatory service functions in Jiangxi Province from 2000 to 2022, which could provide scientific and technological support for effectively guaranteeing ecosystem protection and restoration in Jiangxi Province and improving the quality and efficiency of ecological civilization construction.

Net primary productivity and litter decomposition rates in two distinct Amazonian peatlands.

Measurements of net primary productivity (NPP) and litter decomposition from tropical peatlands are severely lacking, limiting our ability to parameterise and validate models of tropical peatland development and thereby make robust predictions of how these systems will respond to future environmental and climatic change. Here, we present total NPP (i.e., above- and below-ground) and decomposition data from two floristically and structurally distinct forested peatland sites within the Pastaza Marañón Foreland Basin, northern Peru, the largest tropical peatland area in Amazonia: (1) a palm (largely Mauritia flexuosa) dominated swamp forest and (2) a hardwood dominated swamp forest (known as 'pole forest', due to the abundance of thin-stemmed trees). Total NPP in the palm forest and hardwood-dominated forest (9.83 ± 1.43 and 7.34 ± 0.84 Mg C ha-1 year-1, respectively) was low compared with values reported for terra firme forest in the region (14.21-15.01 Mg C ha-1 year-1) and for tropical peatlands elsewhere (11.06 and 13.20 Mg C ha-1 year-1). Despite the similar total NPP of the two forest types, there were considerable differences in the distribution of NPP. Fine root NPP was seven times higher in the palm forest (4.56 ± 1.05 Mg C ha-1 year-1) than in the hardwood forest (0.61 ± 0.22 Mg C ha-1 year-1). Above-ground palm NPP, a frequently overlooked component, made large contributions to total NPP in the palm-dominated forest, accounting for 41% (14% in the hardwood-dominated forest). Conversely, Mauritia flexuosa litter decomposition rates were the same in both plots: highest for leaf material, followed by root and then stem material (21%, 77% and 86% of mass remaining after 1 year respectively for both plots). Our results suggest potential differences in these two peatland types' responses to climate and other environmental changes and will assist in future modelling studies of these systems.

Net primary production annual maxima in the North Atlantic projected to shift in the 21st century

Abstract. Shifts in the day of peak net primary production (NPP) were detected in different biogeochemical provinces of the North Atlantic (25–65° N). Most provinces displayed a shift toward earlier peak NPP, with the largest change points in the 21st century and in the northern parts of the domain. Furthermore, the occurrences of the first day with a mixed-layer depth (MLD) shallower than 40 m and the day of peak NPP are positively correlated over most of the domain. As was the case for the day of peak NPP, the largest change points for the day of MLD shallower than 40 m occur around or after the year 2000. Daily output from two fully coupled CMIP6 Earth system models, EC-Earth3-CC and NorESM2-LM, for the period 1750–2100 and under the SSP5-8.5 scenario, were used for the analysis. The ESM NPP data were compared with estimates derived from Carbon, Absorption and Fluorescence Euphotic-resolving (CAFE) satellite-based data. The ESMs showed significant differences from the CAFE model, though the timing of peak NPP was well captured for most provinces. The largest change points in the day of peak NPP occur earlier in EC-Earth3-CC than in NorESM2-LM. Although SSP5-8.5 is a scenario with very high warming, EC-Earth3-CC generates change points for most provinces in the early part of the 21st century, before the warming has deviated far from lower-emissions scenarios. NorESM2-LM displays the largest change points centered around the mid 21st century, with two out of eight provinces displaying the largest change point before the year 2050. The early timing of the detected shifts in some provinces in both ESMs suggests that similar shifts could already have been initiated or could start in the near future. This highlights the need for long-term monitoring campaigns in the North Atlantic.

Spatiotemporal pattern of NPP and its response to climatic factors in the Yangtze River Economic Belt

Net primary productivity (NPP) of terrestrial ecosystems serves as a crucial carbon sink, playing a significant role in mitigating climate change. This study, focusing on the Yangtze River Economic Belt (YREB), a key area for both economic development and ecological conservation in China, evaluates the temporal and spatial changes in regional NPP and its responses to climatic factors against the backdrop of global change. Utilizing long-term NPP and meteorological grid data from 1981 to 2017, we analyzed the annual change patterns of NPP and its linear and nonlinear responses to climatic factors using copula functions. Our findings indicate that 57.00 % of the YREB has experienced a significant increase in NPP, with the most substantial decreases observed in Shanghai and its surrounding areas (P < 0.05). Temperature emerged as the primary climatic driver, positively correlating with NPP changes in over half of the study area (P < 0.05). Moreover, the nonlinear response of NPP to temperature and precipitation underscores the complex interactions between these factors, revealing a sensitivity to temperature variations that surpasses that to precipitation. The study also highlights the influence of solar radiation and topographic factors, with a significant positive correlation between NPP and solar radiation observed in 15.85 % of the area (P < 0.05). The practical significance of these findings lies in their implications for regional ecological management and climate resilience strategies. The positive correlation between temperature and NPP underscores the potential for targeted temperature management to optimize carbon sequestration across the YREB. Furthermore, the identified nonlinear responses and the role of solar radiation and topography in influencing NPP offer valuable insights for the formulation of adaptive strategies that enhance ecosystem productivity and contribute to achieving carbon neutrality goals. By elucidating the dynamics of NPP in response to a range of environmental factors, this study provides a foundation for informed decision-making in ecological conservation and climate change mitigation efforts within the YREB and similar regions globally.

Exploring the relationship between sea ice and phytoplankton growth in the Weddell Gyre using satellite and Argo float data

Abstract. Some of the highest rates of primary production across the Southern Ocean occur in the seasonal ice zone (SIZ), making this a prominent area of importance for both local ecosystems and the global carbon cycle. There, the annual advance and retreat of ice impacts light and nutrient availability, as well as the circulation and stratification, thereby imposing a dominant control on phytoplankton growth. In this study, the drivers of variability in phytoplankton growth between 2002–2020 in the Weddell Gyre SIZ were assessed using satellite net primary production (NPP) products alongside chlorophyll-a and particulate organic carbon (POC) data from autonomous biogeochemical floats. Although the highest daily rates of NPP are consistently observed in the continental shelf region (water depths shallower than 2000 m), the open-ocean region's larger size and longer ice-free season mean that it dominates biological carbon uptake within the Weddell Gyre, accounting for 93 %–96 % of the basin's total annual NPP. Variability in the summer maximum ice-free area is the strongest predictor of inter-annual variability in total NPP across the Weddell Gyre, with greater ice-free area resulting in greater annual NPP, explaining nearly half of the variance (R2=42 %). In the shelf region, the return of sea ice cover controls the end of the productive season. In the open ocean, however, both satellite NPP and float data show that a decline in NPP occurs before the end of the ice-free season (∼ 80 to 130 d after sea ice retreat). Evidence of concurrent increases in float-observed chlorophyll-a and POC suggest that later in the summer season additional factors such as micro-nutrient availability or top-down controls (e.g. grazing) could be limiting NPP. These results indicate that in a warmer and more ice-free Weddell Gyre, notwithstanding compensating changes in nutrient supply, NPP is likely to be enhanced only up to a certain limit of ice-free days.

Assessing the spatial occupation and ecological impact of human activities in Chengguan district, Lhasa city, Tibetan Plateau

In this study, the ecological impact of human activities and the space occupied by construction and arable land on the Tibetan Plateau were examined, focusing on changes in the net primary productivity (NPP) as a key indicator of ecological health. With the utilization of land use data and multiyear average NPP data from 2002 to 2020, we analyzed the effects of the conversion of zonal vegetation into construction and arable land on carbon sequestration and oxygen release in Chengguan District, Lhasa city. Our findings indicated a marked spatial difference in the NPP among different land types. Regarding the original zonal vegetation, the NPP ranged from 0.2 to 0.3 kg/m2. Construction land showed a decrease in the NPP, with values ranging from 0.16 to 0.26 kg/m2, suggesting a decrease in ecological productivity. Conversely, arable land exhibited an increase in the NPP, with average values exceeding 0.3 kg/m2. This increase suggested enhanced productivity, particularly in regions where the original zonal vegetation provided lower NPP values. However, this enhanced productivity may not necessarily indicate a positive ecological change. In fact, such increases could potentially disrupt the natural balance of ecosystems, leading to unforeseen ecological consequences. The original zonal vegetation, with NPP values ranging from 0.12 to 0.43 kg/m2, exhibited higher ecological stability and adaptability than the other land types. This wider NPP range emphasizes the inherent resilience of native vegetation, which could sustain diverse ecological functions under varying environmental conditions. These findings demonstrate the urgent need for sustainable land use management on the Tibetan Plateau. This study highlights the importance of considering the ecological impact of land use changes in regional development strategies, ensuring the preservation and enhancement in the unique and fragile plateau ecosystem.

TVDI-based water stress coefficient to estimate net primary productivity in soybean areas

Net Primary Productivity (NPP) is a major parameter to assess carbon (C) increments by crops. Many models based on Light Use Efficiency (LUE) have been developed to estimate NPP in different regions. LUE in different ecosystems is reduced by environmental stressors, such as those caused by water restrictions. However, few studies have used remote sensing data to estimate water stress coefficients. Thus, our goal was to evaluate the performance of the Carnegie-Ames-Stanford-Approach (CASA) model using Temperature-Vegetation Dryness Index (TVDI) as a water stress coefficient to quantify NPP dynamics in agricultural ecosystems in northwestern Rio Grande do Sul state, in Brazil. Weather data from the ERA5 and surface weather stations were used to estimate NPP. Surface temperature (LST), Normalized Difference Vegetation Index (NDVI), estimated (EET) and potential (PET) evapotranspiration data were retrieved from Landsat/OLI and Terra/MODIS and used as input in the model. The CASA model was evaluated using ground-based data and then applied to the agricultural region of study. NPP data obtained using the CASA model and remote sensing data were in accordance with the observed data (RMSE less than 30 gC m−2 month−1 and r higher than 0,97), highlighting the model's efficiency in representing the temporal variations of NPP in the experimental area. The high correlation between simulated and observed data indicates that the TVDI is suitable for use as a water stress index. This work may serve as a baseline for future studies, which could explore the use of other indices and enhance NPP estimates.

Spatio-temporal Variation in Net Primary Productivity of Different Vegetation Types and Its Influencing Factors Exploration in Southwest China

Studying the spatiotemporal variation in vegetation net primary productivity (NPP) and exploring its influencing factors are of considerable practical significance for understanding the spatiotemporal variation in vegetation and for guiding ecological restoration and management projects based on local conditions. Based on MODIS NPP data, combined with in situ meteorological data, land use data, and vegetation type data, this study explores the spatiotemporal variation in different types of vegetation NPP in southwest China via the Mann-Kendall significance test and Theil-Sen Median slope estimator. It reveals the influencing factors of spatial differentiation of different types of vegetation NPP and the interaction between influencing factors in combination with stability analysis and Geo Detectors. The results revealed that on the temporal scale, from 2000 to 2021, vegetation NPP, NPPPre (vegetation NPP exclusively under the influence of climate change), and NPPRes (vegetation NPP exclusively under the influence of human activities) in southwest China showed a fluctuating upward trend. Among different vegetation types, NPP, NPPPre, and NPPRes exhibited an upward trend, except for a minor decline in NPPRes of tree vegetation at a rate of -0.183 g·(m2·a)-1. Among them, NPP, NPPPre, and NPPRes of economic vegetation showed the most significant upward rates, 5.96, 3.09, and 2.94 g·(m2·a)-1, respectively. On the spatial scale, the tree vegetation NPP with the most significant downward trend was mainly distributed in Tibet and southern Yunnan, while the economic vegetation NPP with the highest upward trend was primarily distributed in eastern Sichuan Province. The stability of vegetation NPP in southwest China presented a spatial distribution pattern of "low in the south and high in the north," and the average value of the correlation coefficient increased in the ascending order of arbor vegetation (0.101), shrub vegetation (0.105), herb vegetation (0.110), and economic vegetation (0.114). The interaction between surface temperature and relative humidity was the main influencing factor for spatial differentiation of vegetation NPP, while the interaction between sunshine duration and warmth index had the most significant impact on vegetation in southwest China, with an increasing percentage of 30.91%. Different types of vegetation had different requirements for different climatic factors, but their requirements for surface temperature and warmth index were significantly consistent. When the surface temperature was 21.03-28.49℃, and the warmth index was 106.46-167.2, the NPP of different vegetation types peaked. Under natural succession, the impact of climate change on vegetation was inversely proportional to the stability of the vegetation community. The arbor vegetation community with high stability was less affected, while the herb vegetation community with low stability was highly affected by climate. In contrast, the stability of economic vegetation was directly proportional to the impact of climate due to the influence of human activities. This study establishes a theoretical foundation for evaluating the impact of regional climate on the growth of different vegetation types and can be crucial for formulating ecological restoration and management strategies in southwest China that are adapted to the local conditions.

Spatial and temporal variation of net primary productivity of herbaceous marshes and its climatic drivers in China.

Herbaceous marshes are widely distributed in China and are vital to regional ecological security and sustainable development. Vegetation net primary productivity (NPP) is a vital indicator of vegetation growth. Climatic change can significantly affect NPP, but variations in NPP of herbaceous marsh and their responses to climate change in China remain unclear. Using meteorological data and MODIS NPP data during 2000-2020, this study analyzed the spatial and temporal variations of NPP and their responses to climate change in Chinese herbaceous marshes. We found that the annual NPP of herbaceous marshes in China increased significantly at a rate of 3.34 g C/m2/a from 2000 to 2020, with an average value of 336.60 g C/m2. The increased annual total precipitation enhanced the national average NPP, whereas annual mean temperature had no significant effect on the national average NPP. Regionally, precipitation had a significant positive effect on the NPP in temperate semi-arid and arid and temperate semi-humid and humid marsh regions. For the first time, we discovered asymmetry effects of daytime and nighttime temperatures on NPP in herbaceous marshes of China. In temperate humid and semi-humid marsh regions, increased summer daytime temperature decreased the NPP while increased summer nighttime temperature increased the NPP. In the Tibetan Plateau, increased autumn daytime temperature, as well as summer daytime and nighttime temperatures could increase the NPP of herbaceous marshes. This study highlights the different influences of seasonal climate change on the NPP of herbaceous marshes in China and indicates that the differential effects of daytime and nighttime temperatures should be considering in simulating the NPP of herbaceous marshes in terrestrial ecosystem models, especially under the background of global asymmetric diurnal warming.

Trends and contribution of different grassland types in restoring the Three River Headwater Region, China, 1988–2012

Rapid greening of the Qinghai–Tibet Plateau had been confirmed, but the contributions to the overall change and its causes in various grassland types has been less studied. Previous research has focused on exogenous factors such as climate change and human activities, rather than on endogenous factors, such as grassland types. Using net primary productivity (NPP), precipitation and temperature data, we applied trend, contribution and pull contribution analysis to understand the spatiotemporal evolution and driving factors of six different grassland types at a pixel scale in the Three River Headwater Region (TRHR) of China from 1988 to 2012. The results showed that grassland NPP in the TRHR increased at an average growth amount of 3.46 gC m−2 yr−1 and an average growth rate of 2.26 %. The average growth amount of alpine desert and alpine steppe (0.42 gC m−2 yr−1, 1.74 gC m−2 yr−1, respectively) showed great potential improvement. The average growth rate (1.27 %, 1.87 %) of montane meadow and alpine meadow, respectively, presented a high potential to increase (P < 0.05). Alpine meadow, montane meadow and temperate steppe were positive pullers to the average growth amount. Alpine steppe and alpine desert were positive pullers to the average growth rate. In general, alpine meadow had the highest growth amount contribution (84.86 %), while alpine meadow and alpine steppe had the highest contribution to the growth rate (62.16 %, 34.24 %, respectively). The study implied that, in addition to external factors, differences in internal factors such as the community composition and structure of different grassland types could also affected the grassland recovery process. These results contribute to understanding the specific differences in the contribution of regional grassland restoration processes from vegetation composition. Assessing grasslands with the potential to increase productivity, we can provide scientific reference for implementing more precise and efficient measures in future grassland management restoration.

Spatio-temporal variation and impacting factors of NPP from 2001 to 2020 in Sanjiangyuan region, China: A deep neural network-based quantitative estimation approach

The ecosystem in the Sanjiangyuan region of China is known to all for its simplicity and fragility. Studying the changes in net primary productivity (NPP) of vegetation and its influencing factors will help protect and improve this fragile environment. However, there are missing data in some regions in NPP data collectors, which affect the data availability. Therefore, this article proposes a method for estimating NPP using only a small amount of basic data by constructing a deep neural network (DNN) model suitable for NPP estimation. This method requires only a small amount of basic data to obtain high-precision, complete NPP data. We conducted a precision verification of the NPP estimates in the Sanjiangyuan region and studied the temporal and spatial variation of NPP in the Sanjiangyuan region, the relations between NPP and meteorological factors, and the contributions of climate change and human activities to NPP changes. The results showed that the estimation of NPP was reliable, with coefficient of determination (R2) ranging from 0.893 to 0.933 and root mean square error (RMSE) ranging from 24.904 to 34.550. From 2001 to 2020, the NPP in the study area showed a significant increasing trend (1.32 g C/m2·year−1, P < 0.05). The partial correlation coefficients between climate and NPP were all positive, with the maximum partial correlation coefficient between temperature and NPP (0.308), followed by radiation (0.113) and precipitation (0.067). The contributions of climate change and human activities to NPP changes were 1.298 and − 0.051 g C/m2·year−1, respectively. Climate change was the main driving factor for vegetation regeneration, while human factors were the main cause of vegetation reduction in the study area.