Estimates Of Net Primary Production Research Articles

A new method of high-resolution NPP estimation and its implementation in caragana shrublands on the Loess Plateau, China

ABSTRACT Accurate estimation for vegetation net primary production (NPP) at high resolution is essential for ecosystem management and at local to global scales. However, no reliable methods are available to achieve fine-resolution estimation. In this study, a new NPP estimation system was developed by integrating high spatial resolution remote sensing images, tree ring measurements, and process-based model CASA. The system was illustrated by estimating the historical NPP of caragana shrublands from 2011 to 2021 in Dingbian County, China. The results showed that: (1) by employing the biomass model and tree ring measurements, an NPP-crown width model was established to enable a large number of samples for calibrating the CASA model on high spatial resolution images without continuous field measurements. The calibrated parameters, including maximum NDVI, minimum NDVI, and maximum light use efficiency, were determined to be 0.415, 0.022 and 0.025, respectively; (2) according to the estimation by the calibrated CASA model, the NPP in Dingbian County revealed an increasing trend from 2011 to 2021, and the NPP in southern hilly and gully regions was significantly higher than the other regions; (3) among the different indices, max temperature of warmest month had a significant negative effect on the NPP of cagarana shrublands, whereas annual precipitation had a positive effect. In the northern and central regions, air temperature predominantly influenced NPP, while in the southern regions, both temperature and precipitation played a joint role. The implementation in this study illustrated that the NPP estimation system could provide reliable NPP with high resolution and efficient to monitor and detect dominant factors affecting NPP for ecosystem management.

Estimation of Corn Net Primary Productivity and Carbon Sequestration Based on the CASA Model: A Case Study of the Fen River Basin

The utilization of remote sensing technology to assess changes in crop net primary productivity (NPP), biomass, and carbon sequestration within the Fen River Basin, a crucial agricultural region in China, is important for achieving agricultural modernization, enhancing ecological environment quality, and obtaining carbon neutrality objectives. This study employed satellite remote sensing and the Carnegie–Ames–Stanford approach (CASA) model as research methods to investigate the temporal and spatial distribution characteristics of corn NPP in the Fen River Basin. Correlation analysis was conducted to examine the response of corn NPP to various environmental factors in the region, while aboveground biomass and carbon sequestration of corn were estimated using a biomass inversion model driven by NPP and principles of photosynthesis in green plants. The findings revealed that, from a temporal perspective, corn NPP in the Fen River Basin exhibited a unimodal variation pattern, with an average value of 368.65 gC/m2. Spatially, the corn NPP displayed a discernible differentiation pattern, with the highest values primarily observed in the middle reaches of the Fen River Basin. Throughout the spatial and temporal variations in corn NPP during 2011–2020, the carbon sequestration capacity of corn exhibited an upward trend, particularly since 2017. The corn NPP displayed a positive correlation with temperature and precipitation. The response to solar radiation was mildly negative and a mildly positive correlation. In 2020, the aboveground biomass and carbon sequestration of corn followed a normal distribution, with the highest values concentrated in the northwestern part of the lower Fen River.

Estimation of cucumber net primary production using environmental and control information in a smart multi-span plastic greenhouse

A smart greenhouse can be considered as a very large chamber under closed conditions. The amount of change in CO2 concentration inside the greenhouse during periods without CO2 fertilization depends on the net primary production (i.e., net carbon assimilated by plants via photosynthesis) (NPP) of the crops grown in the greenhouse. In this study, we estimated the NPP of cucumbers in a small-scale smart multi-span plastic greenhouse using environmental and control information collected during a low-temperature period when the greenhouse was frequently closed. The timescale for robustly calculating NPP using the temporal change in the CO2 concentration inside the greenhouse was empirically determined (=10 min), and a random forest-based NPP model was established using the calculated NPP and internal and external environmental greenhouse data to estimate NPP, even in the cases of opening roof windows or supplying CO2. The estimated NPP showed previously known relationships with environmental variables in the greenhouse, and cumulative NPP was consistent with cucumber growth. Based on the CO2 balance equation of a smart greenhouse and estimated NPP, we quantified the amount of CO2 supplied and CO2 emissions via the roof windows. The CO2 budget estimation errors and suggestions for reducing errors were discussed. The quantified CO2 budget in the greenhouse, including NPP, can be used for evaluating economic feasibility and improving crop productivity.

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.

Assessing the Distribution and Driving Effects of Net Primary Productivity along an Elevation Gradient in Subtropical Regions of China

Globally, forest ecosystems, especially subtropical forests, play a central role in biogeochemical cycles and climate regulation, demonstrating their irreplaceable function. The subtropical region of China, characterized by its unique forest ecosystem, complex terrain, climate heterogeneity, diverse vegetation types, and frequent human activities, underscores the importance of the in-depth study of its net primary productivity (NPP). This paper employs the eddy covariance–light use efficiency (EC-LUE) model to quantitatively estimate the gross primary productivity (GPP) of this region from 2001 to 2018, followed by an estimation of the actual net primary productivity (ANPP) using the carbon use efficiency (CUE). The results showed that over these 18 years, the annual average ANPP was 677.17 gC m−2 a−1, exhibiting an overall increasing trend, particularly in mountainous areas, reserves, and the cultivated lands of the northeastern plains, whereas a significant decrease was observed around the urban agglomerations on the southeast coast. Furthermore, the Thornthwaite memorial model was applied to calculate the potential net primary productivity (PNPP), and diverse scenarios were set to quantitatively evaluate the impact of climate change and human activities on the vegetation productivity in the study area. It was found that in areas where the ANPP increased, both human activities and climate change jointly influenced ANPP dynamics; in areas with a decreased ANPP, the impact of human activities was particularly significant. Additionally, the heterogeneous distribution of ANPP across different altitudinal gradients and the driving effects of various climatic factors were analyzed. Finally, a partial correlation analysis was used to examine the relationships between the temperature, precipitation, and ANPP. This study indicated that temperature and precipitation have a substantial impact on the growth and distribution of vegetation in the region, yet the extent of this influence shows considerable variation among different areas. This provides a robust scientific basis for further research and understanding of the carbon dynamics of subtropical forest ecosystems and their role in the global carbon cycle.

Vertical stratification of phytoplankton biomass in a deep estuary site: implications for satellite-based net primary productivity

The accuracy of satellite estimates for water column net primary productivity (NPP) are contingent upon the reliability of surface phytoplankton biomass, specifically chlorophyll a (Chl.a) and carbon (Cphyt), as indicators of euphotic biomass and photosynthetic rate. We assessed patterns in water column biomass at a deep estuary site (~40 m) in the Firth of Thames, Hauraki Gulf, New Zealand, using ten years (2005-2015) of in situ sampling (40 seasonal voyages and moored instrumentation). Seasonal biomass stratification coincided with physical and chemical stratification and exhibited a reasonable predictability based on surface Chl.a measures from mooring timeseries. High Chl.a (but not Cphyt) accumulated from late-spring (Nov.) in the lower portion of the water column, under nutrient deficient, clear surface water with deep euphotic zone conditions, peaking in mid-summer (Jan.) and ending by early autumn (Mar.). Satellite (MODIS-Aqua) NPP (2002-2018), was estimated with and without correction for deep biomass in two vertically generalized production models (Chl.a-VGPM and Cphyt-CbPM). Mean annual NPP (220-161 g C m-2 y-1, VGPM and CbPM respectively) increased 5-18% after accounting for euphotic zone deep biomass with a mid-summer maxim (Jan.: 30-33%). Interannual anomalies in biomass and NPP (about -10% to 10%) were an order of magnitude greater than small decreasing trends (<< 1% y-1). We discuss the impacts of observational factors on biomass and NPP estimation. We offer contextual insights into seasonal patterns by considering previous observations of biomass trends and nutrient enrichment in the Firth of Thames region. We propose future directions in accounting for deep biomass variations from shallow coastal areas to deeper continental shelf waters.

NDVI joint process-based models drive a learning ensemble model for accurately estimating cropland net primary productivity (NPP)

The accurate estimation of cropland net primary productivity (NPP) remains a significant challenge. We hypothesized that incorporating prior information on NPP simulated by process-based models into normalized difference vegetation index (NDVI) data would improve the accuracy of cropland ecosystem NPP estimations. We used NDVI, MNPP (NPP of process-based model), and SNPP (statistic-based NPP) data estimated by nine process-based models and yield statistics to build a learning ensemble of the random forest model (LERFM). We used the new model to re-evaluate the cropland NPP in China from 1982 to 2010. Large spatial discrepancies among MNPPs, which indicate uncertainties in cropland NPP estimation using different methods, were observed when compared to SNPP. The LERFM model showed a slightly underestimation of only −0.37%, while the multi-model average process-based model (MMEM) strongly underestimated −15.46% of the SNPP. LERFM accurately estimated cropland NPP with a high simulation skill score. A consistent increasing trend in the LERFM and MMEM NPP during 1982–2010 and a significant positive correlation (r = 0.795, p < 0.001) between their total NPP indicate that the LERFM model can better describe spatiotemporal dynamic changes in cropland NPP. This study suggests that a learning ensemble method that combines the NDVI and process-based simulation results can effectively improve cropland NPP.

Evaluation of three contrasting models in estimating primary production from ocean color remote sensing using long-term time-series data at oceanic and coastal sites

Accurate estimates of depth-integrated Net Primary Production (NPP, mg C m−2 d−1) and the creation of a robust climate data record of NPP for the global oceans are essential goals of the ocean color remote sensing community. Here, we take advantage of in situ NPP measurements from three long-term time-series sites, the HOT (Hawaii Ocean Time-series), BATS (Bermuda Atlantic Time-series Study) and CARIACO (Ocean Time-Series Program from the Cariaco basin), spanning over 30 years to evaluate three contrasting models in estimating NPP from ocean color remote sensing. These models for NPP estimation include the Absorption-based Model (AbPM), which relies on phytoplankton absorption coefficient, the Vertically Generalized Production Model (VGPM), which centers on chlorophyll-a concentration, and the Carbon-based Productivity Model (CbPM) centering on phytoplankton carbon. In addition to the accuracy of NPP estimation from these models, we laid great emphasis on evaluating their skills in capturing the monthly to seasonal variations and interannual trends in NPP at the three sites. Comparison with in situ NPP at all three long-term sites (∼20 years) showed that AbPM yielded the highest coefficient of determination (R2 = 0.67) and the lowest uncertainties (Bias = 0.03 and unbiased root mean square difference = 0.17). Seasonal and interannual variations apparent in the in situ NPP time-series records were best captured by AbPM. These results showcase the robust capabilities of AbPM and its superiority for global carbon cycling and climate change studies, largely because it takes into account optical and photosynthetic parameters of local phytoplankton.

Multi-source data-driven estimation of urban net primary productivity: A case study of Wuhan

Amidst urban expansion, numerous ecological challenges have surfaced, notably the swift alterations in the net primary productivity (NPP), presenting complexities in discerning their scale and spatial distribution. Leveraging meteorological data, multi-modal remote sensing datasets, field sampling data, and a digital elevation model (DEM), this research amalgamates field assessments, model simulations, and cutting-edge remote sensing techniques to craft a city-scale NPP estimation model for vegetation rooted in the Carnegie-Ames-Stanford Approach (CASA). This improved CASA model facilitated the computation of monthly NPP for Wuhan over 2017–2020. Our analysis divulged NPP spatiotemporal trends and distribution nuances in Wuhan, and assessed meteorological influences on the same. Salient conclusions encompass: (1) Wuhan's annual NPP maximum between 984.40 and 1310.51 gC/(m2 a), averaging from 316.78 to 430.32 gC/(m2 a), and culminating in totals from 170.15 to 231.60 × 1010 gC/a. (2) Monthly NPP trajectories in Wuhan exhibited a unimodal pattern: ascending from January to June, peaking in July–August, and tapering off through September-December, with a spatial distribution characterized by lower NPP values centrally and higher values peripherally. (3) Regionally, temperature markedly influenced NPP over precipitation. This research underpins urban ecological planning, offering empirical insights and strategic guidance for fostering a sustainable coexistence between humans and their environment, and charting future urban development paradigms.

Assessing net primary production in the northwestern Barents Sea using in situ, remote sensing and modelling approaches

The northwestern Barents Sea (NW-BS) is a highly productive region within the transitional zones of an Atlantic to Arctic-dominated marine ecosystem. The steep latitudinal gradients in sea ice concentration, Atlantic and Arctic Water, offer an opportunity to test hypotheses on physical drivers of spatial and temporal variability of net primary production (NPP). However, quantifying NPP in such a large ocean region can be challenged by the lack of in situ measurements with high spatial and temporal resolution, and gaps in remote sensing estimates due to the presence of clouds and sea ice, and assumptions regarding the depth distribution of alga biomass. Without reliable data to evaluate models, filling these gaps with numerical models is limited by the model representation of the physical environment and its assumptions about the relationships between NPP and its main limiting factors. Hence, within the framework of the Nansen Legacy Project, we combined in situ measurements, remote sensing, and model simulations to constrain the estimates of phytoplankton NPP in the NW-BS. The region was subdivided into Atlantic, Subarctic, and Arctic subregions on the basis of different phytoplankton phenology. In 2004 there was a significant regime change in the Atlantic subregion that resulted in a step-increase in NPP in tandem with a step-decrease in sea ice concentration. Contrary to results from other Arctic seas, this study does not find any long term trends in NPP despite changes in the physical environment. Mixing was the main driver of simulated annual NPP in the Atlantic subregion, while light and nutrients drove annual NPP in the Subarctic and Arctic subregions. The multi-source estimate of annual NPP ranged 79–118gCm−2yr−1 in the Atlantic, 74–82gCm−2yr−1 in the Subarctic, and 19–47gCm−2yr−1 in the Arctic. The total NPP in the NW-BS region was estimated between 15 and 48TgCyr−1, which is 15–50% of the total NPP needed to sustain three of the most harvested fish species north of 62°N (roughly 90TgCyr−1). This research shows the importance of continuing to strive for better regional estimates of NPP.

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.

Analysis of spatial and temporal variation of vegetation NPP in Daning River Basin and its driving forces

ABSTRACT The Daning River Basin is a typical representative of the ‘mountain forest’ in the Three Gorges Reservoir (TGR) area of the Yangtze River. In recent years, with the completion of the Three Gorges Project, the local vegetation has degraded, soil erosion has become severe, and there is an urgent need to assess the environmental quality. Data were fused using the MODIS Normalized Difference Vegetation Index (NDVI) (250 m) and Landsat NDVI (30 m) through an Enhanced Spatial and Temporal Adaptive Reflectance Fusion Model (ESTARFM) to obtain ESTARFM NDVI (30 m). This data was then entered into the Carnegie Ames Stanford Approach (CASA) model, along with meteorological and land use data, to calculate vegetation net primary productivity (NPP) and trends in the Daning River Basin. The detection of drivers with a high impact on vegetation NPP was done using a Geodetector. The results show that: (1) In terms of spatial and temporal changes, the annual average NPP of the watershed during the 13 years from 2008 to 2020 generally showed an upward trend, with the average yearly vegetation NPP being 512.33gC·m−2·a−1, exhibiting a low southwest to surrounding increasing trend along the river. (2) The spatial and temporal variation of vegetation NPP is influenced by several factors synergistically, with elevation, temperature, and distance from settlements being the dominant factors. The interaction of these two factors can enhance the explanatory power of vegetation NPP. Through the estimation of vegetation NPP and the analysis of influencing factors in the Daning River Basin, this study can provide a reference for the ecological restoration and management of vegetation NPP in small watersheds under similar environments.

Impact of subsurface chlorophyll maxima on satellite-based Arctic spring primary production estimates

In recent years several regional and pan-Arctic ocean color algorithms, which consider the unique bio-optical properties of the Arctic Ocean, have been developed to accurately extract surface chlorophyll a (Chl a), a key input into net primary production (NPP) algorithms, from spectral remote sensing reflectance (Rrs). However, most satellite derived NPP (NPPSAT) algorithms used in the Arctic do not account for production at deep subsurface chlorophyll maxima (SCMs), a prominent feature in the Arctic Ocean during the summer months, leading to underestimations of NPP during the post-bloom period. In this study, a shallow SCM was observed in early summer (June/July 2016) in Baffin Bay at the time of the sea ice edge bloom, raising the question to what extent this SCM contributes to Rrs and how it is captured in NPPSAT estimates. Radiative transfer simulations based on in situ data of inherent and apparent optical properties and Chl a concentration in the water column were used to examine the effects of heterogeneous vertical Chl a profiles of various strength and depth on spectral reflectance, algorithm-derived surface Chl aSAT and NPPSAT. NPPSAT estimates for varying Chl a profile shapes were then compared to NPPSAT estimates of reference simulations for homogenous Chl a profiles, and to measured NPP calculated using observed Chl a profiles. Results show a significant impact of shallow SCMs on Rrs with the depth of maximum Chl a < 30 m, causing an increase in Chl aSAT by 17 ± 6% relative to a vertically homogenous ocean. Interestingly, SCMs significantly influencing Rrs were found down to the fifth light penetration depth. The increase in Chl aSAT reduced the difference between predicted and measured NPP estimates to −25 ± 12% at stations with a shallow SCM. Furthermore, due to the significant contribution of these shallow SCM stations to regional NPP, regional predicted NPPSAT was only 16% lower than measured NPP. Our results demonstrate that the partial representation of a shallow and very productive SCM in Chl aSAT minimizes errors in satellite-based NPPSAT estimates of the Arctic spring bloom during the sea ice melt period.

Continuity of Global MODIS Terrestrial Primary Productivity Estimates in the VIIRS Era Using Model‐Data Fusion

AbstractThe NASA Terra and Aqua satellites have been successfully operating for over two decades, exceeding their original design life. However, the era of NASA's Earth Observing System may be coming to a close as early as 2023. Similarities between the Moderate Resolution Imaging Spectroradiometer (MODIS), aboard Aqua and Terra, and the Visible Infrared Imaging Radiometer Suite (VIIRS) sensors aboard the Suomi NPP, NOAA‐20 and NOAA‐21 satellites enable potential continuity of long‐term earth observational records in the VIIRS era. We conducted a comprehensive calibration and validation of the MODIS MOD17 product, which provided the first global, continuous, weekly estimates of ecosystem gross primary productivity (GPP) and annual estimates of net primary productivity (NPP). Using Bayesian model‐data fusion, we combined 18 years of tower fluxes with prior data on plant traits and hundreds of field measurements of NPP to benchmark MOD17 and to develop the first terrestrial productivity estimates from VIIRS. The updated mean global GPP (NPP) flux from the future MOD17 Collection 7 product and new VNP17 product for 2012–2018 is 127 ± 2.8 Pg C year−1 (58 ± 1.1 Pg C year−1), which compares well with independent top‐down and bottom‐up estimates. MOD17 and VNP17 depict upward productivity trends over recent decades, with 2000–2018 MOD17 GPP (NPP) rising by 0.47 (0.25) Pg C year−2 but slowing to 0.35–0.44 (0.11–0.13) Pg C year−2 over 2012–2021, with a greater reduction in the NPP growth rate. The new VIIRS VNP17 product has the potential to extend these estimates of global, terrestrial primary productivity beyond 2030.

Equus suessenbornensis from Akhalkalaki (Georgia, Caucasus): a review with new insights on the paleoecology, paleobiogeography and evolution of the palearctic large-sized equids during the Early – Middle Pleistocene Transition

Equus suessebornensis is one of the most intriguing species of the Early and Middle Pleistocene Eurasian Equus. Although it was described from the Middle Pleistocene site of Süssenborn, in recent years its biochronological correlation has been extended to the Early Pleistocene, raising a debate in the scientific community. In this contribution, we provide a complete ontogenically-based description of the Akhalkalaki E. suessenbornensis samples, and we compare it by multivariate and statistical analyses with the Early and Middle Pleistocene European fossil record. Moreover, we provide new paleoecological insights utilizing body mass, mesowear and net primary productivity estimates. Our outcomes provide a new perspective on the E. suessenbornensis morphology and variability, but also new interpretations for its paleoecology, biochronology, paleobiogeography and evolution. Eventually, these results provide a reassessment of equid evolution during the Early – Middle Pleistocene Transition in Western Eurasia, a critical period for the dynamics of the Quaternary faunas and paleoenvironments.

NPP and Vegetation Carbon Sink Capacity Estimation of Urban Green Space Using the Optimized CASA Model: A Case Study of Five Chinese Cities

Urban area is a major source of CO2 and other greenhouse gases. Urban green space (UGS) is an essential element to increase carbon sequestration directly and reduce emission indirectly. In this study, the net primary production (NPP) and net ecosystem productivity (NEP) was monitored in order to enhance the carbon sequestration function of UGS and promote urban low-carbon development. Based on the Sentinel-2 L2A satellite images, meteorological data, and vegetation type data in 2019, we used the optimized Carnegie Ames Stanford Approach (CASA) model to estimate the NPP values of UGS types including attached green space, park green space, protective green space, and regional space in Beijing, Guangzhou, Shanghai, Shenyang, and Xi’an. The NEP values were evaluated based on NPP and soil heterotrophic respiration (RH) to quantify the vegetation carbon sink capacity. The accuracy test shows that the estimated NPP values based on the optimized CASA model are effective. The results indicate that the average NPP values (1008.5 gC·m−2·a−1) and vegetation carbon sink capacity (771.49 gC·m−2·a−1) of UGS in Beijing rank first among the cities, which is followed by the values in Guangzhou. The regional green space and park green space in five cities function as carbon sinks with high NPP values and have vegetation carbon sink capacity, whereas the attached green space in Shanghai and Xi’an as well as the protective green space in Guangzhou and Xi’an function as carbon sources. Moreover, the NEP distribution shows obvious spatial aggregation characteristics, that is, the high NEP values of UGS are clustered in mountainous forest areas in the west and north of Beijing, Northeast Guangzhou, and South Xi’an whereas the low NEP values are mostly concentrated in the urban built-up areas under strong influences of human activities. This research provides a new method for NPP and NEP estimation of UGS at the city scale and the scientific basis for the improvement of the vegetation carbon sink capacity of UGS.

Exploring the Potential of Solar-Induced Chlorophyll Fluorescence Monitoring Drought-Induced Net Primary Productivity Dynamics in the Huang-Huai-Hai Plain Based on the SIF/NPP Ratio

Drought causes significant losses in vegetation net primary productivity (NPP). However, the lack of real-time, large-scale NPP data poses challenges in analyzing the relationship between drought and NPP. Solar-induced chlorophyll fluorescence (SIF) offers a real-time approach to monitoring drought-induced NPP dynamics. Using two drought events in the Huang–Huai–Hai Plain from 2010 to 2020 as examples, we propose a new SIF/NPP ratio index to quantify and evaluate SIF’s capability in monitoring drought-induced NPP dynamics. The findings reveal distinct seasonal changes in the SIF/NPP ratio across different drought events, intensities, and time scales. SIF demonstrates high sensitivity to commonly used vegetation greenness parameters for NPP estimation (R2 &gt; 0.8, p &lt; 0.01 for SIF vs NDVI and SIF vs LAI), as well as moderate sensitivity to land surface temperature (LST) and a fraction of absorbed photosynthetically active radiation (FAPAR) (R2 &gt; 0.5, p &lt; 0.01 for SIF vs FAPAR and R2 &gt; 0.6, p &lt; 0.01 for SIF vs LST). However, SIF shows limited sensitivity to precipitation (PRE). Our study suggests that SIF has potential for monitoring drought-induced NPP dynamics, offering a new approach for real-time monitoring and enhancing understanding of the drought–vegetation productivity relationship.

Seasonal dynamics of herbaceous layer biomass and its contribution to annual net primary production in an oak–hornbeam forest

The role of the herbaceous layer in carbon assimilation and nutrient cycling is still undiscovered and unappreciated. One reason is that simple methods of annual net primary production (ANPP) estimation often underestimate its value, while more accurate methods are too labor-demanding. Moreover, studies on herbaceous layer and litterfall biomass in the same study site are extremely rare and usually conducted during one vegetation season, providing temporally-specific estimates. We hypothesized that (1) the seasonal herb layer biomass dynamics show a two-peak shape and (2) contribution of herbaceous layer biomass production in annual litter production was not more than 5% of tree litterfall. We studied the herb layer and litterfall biomass dynamics in an oak-hornbeam forest in W Poland for three years (January 2013 – October 2015). Samples of the herb layer (ten samples for four sample plots) were collected weekly in March, April and May or every two weeks for the remainder of each year, and litterfall was collected every month. All herb plants were cut at the soil level from circular frames with an area of 0.16 m2. Litterfall was studied with thirty litter traps (height of 15 cm, and catching area of 0.36 m2). We performed Kruskal–Wallis tests with Dunn’s post hoc tests to assess differences in variables among all collection times and linear random effects models to assess variance sources. We observed the highest biomass in 2013 on May 3rd (47.05 g m−2), and the lowest on October 30th in 2015 (4.69 g m−2). The seasonal herb layer biomass dynamic showed a single-peak shape; a very distinct peak occurred in spring and continued in late spring, summer, and early autumn. The spring ephemeralAnemone nemorosahad the highest share of herbaceous layer ANPP with 27% and was responsible for the spring biomass peak every season. The mean contribution of the herb layer biomass in annual litterfall was 9.23%. Our results highlight the underestimation of the herb layer role in biomass production in forest ecosystems in previous studies.

Estimation and Climate Impact Analysis of Terrestrial Vegetation Net Primary Productivity in China from 2001 to 2020

The net primary productivity (NPP) of vegetation is an important indicator reflecting the vegetation dynamics and carbon sequestration capacity in a region. In recent years, China has implemented policies to carry out ecological protection. To understand the changes in the distribution of vegetation NPP in China and the influence of climate factors, the Carnegie–Ames–Stanford approach (CASA) model was used to estimate the NPP from 2001 to 2020. In this paper, several sets of measurement datasets and products were collected to evaluate the effectiveness of the model and suggestions were provided for the modification of the CASA model based on the evaluation results. In addition to the correlation analysis, this paper presents a statistical method for analyzing the quantitative effects in individual climatic factors on NPP changes in large regions. The comparison found that the model has a better estimation effect on grassland and needleleaf forest. The estimation error for the evergreen needleleaf forest (ENF) and deciduous broadleaf forest (DBF) decreases with the warming of the climatic zone, while the evergreen broadleaf forest (EBF) and deciduous needleleaf forest (DNF) do the opposite. The changes in total CASA NPP were consistent with the trends of other products, showing a dynamic increasing trend. In terms of the degree of correlation between the NPP changes and climatic factors, the NPP changes were significantly correlated with temperature in about 10.39% of the vegetation cover area and with precipitation in about 26.92% of the vegetation cover area. It was found that the NPP variation had a negative response to the temperature variation in Inner Mongolia grasslands, while it had a positive but small effect (±10 g C) in the Qinghai–Tibet Plateau grasslands. Precipitation had a facilitative effect on the grassland NPP variation, while an increase in the annual precipitation of more than 200 mm had an inhibitory effect in arid and semi-arid regions. This study can provide data and methodological reference for the ecological assessment of large-scale regional and climate anomalous environments.

Spatial patterns of China's carbon sinks estimated from the fusion of remote sensing and field-observed net primary productivity and heterotrophic respiration

Accurately assessing the carbon sink and spatial distribution pattern of China's terrestrial ecosystems is of great significance to the implementation of climate change and carbon neutrality strategy. However, the views of various studies are still very controversial due to the differences in carbon sink estimation methods and data sources. In this study, vegetation net primary productivity (NPP) and ecosystem heterotrophic respiration (Rh) estimation models were constructed based on machine learning methods by fusing multisource data, such as remote sensing and ground observation data. The magnitude and spatial pattern of carbon sink in China from 2000 to 2018 were then revealed, and the carbon sink capacity of various ecosystems was quantitatively assessed. The main conclusions include the following: (1) The use of scale-matched carbon input and output data can help reduce the system error in carbon sink estimation. (2) China's terrestrial ecosystem carbon sink since the twenty-first century is approximately 0.458 Pg C/yr, which is equivalent to 22.72% of China's anthropogenic carbon emissions. (3) Deciduous forest has a higher carbon sink capacity than evergreen forest, while coniferous forest has a more stable carbon sink capacity than broad-leaved forest. The magnitude and spatial distribution of carbon sink in China reported in this study provides a scientific reference for achieving carbon neutrality and sustainable development.