Estimates Of Net Primary Production Research Articles

Estimates of diurnal and daily net primary productivity using the Geostationary Ocean Color Imager (GOCI) data

For the past three decades, polar-orbiting ocean color satellites have provided local, regional to global scale estimates of oceanic net primary production that have greatly aided studies of ocean carbon cycling, food web dynamics and climate change. Despite considerable progress, accurate estimates of daily ocean productivity from space have not been realized because polar-orbiting satellites are unable to account for variations in phytoplankton physiology and carbon fixation rates from changing incident irradiance levels over the course of the day. Here we have attempted to exploit the unique short-temporal measurements provided by the Korean Geostationary Ocean Color Imager (GOCI), to obtain for the first time, diurnal and daily measurements of surface- and euphotic-column integrated Net Primary Productivity (NPP). These estimates are based on the Absorption Based Productivity Model (AbPM), and are calibrated with a bio-optical database of measurements made at different times of the day during the Korea-US Ocean Color (KORUS-OC) cruise in May–June 2016. Photo-physiological rate estimates, varied across different water types encountered around the Korean Peninsula, but on account of their limited number, were regionally scaled through the use of dynamic optical-biogeochemical (O-BGC) Biomes. Comparisons of GOCI derived estimates of daily and weekly integrated NPP against in-situ measurements, clearly underscore the superiority of geostationary over polar-orbiting ocean color satellites, which consider the strong short-term fluctuations in phytoplankton light absorption and incident irradiance fields over the course of the day.

Seasonal cycles of phytoplankton and net primary production from Biogeochemical Argo float data in the south-west Pacific Ocean

We present annual cycles of chlorophyll a, phytoplankton carbon, nitrate and oxygen for Subtropical (STW), Subantarctic (SAW), and Subantarctic Mode (SAMW) waters near Aotearoa New Zealand from data collected by two Biogeochemical (BGC) Argo floats. We develop two simple models of depth-integrated net primary production (NPP), tuned against 14C-uptake measurements, to compare with Vertically-Generalised Production Model (VGPM) satellite-based estimates of NPP. One model is the simplest possible, and assumes production is proportional to light multiplied by chlorophyll a concentration. The second model modifies the light response profile to account for photoacclimation. In STW at 30–35°S, enhanced production is initiated in austral autumn when the mixed layer deepens to entrain nutrients into the photic zone. For about half the year, there is substantial production within a deep chlorophyll maximum that sits below the mixed layer. Consequently, depth-integrated NPP is only loosely related to surface biomass as imaged from satellite remote-sensing, and BGC Argo-based model estimates of depth-integrated NPP are about double VGPM estimates. In SAW at 45–55°S, production is initiated when vertical mixing decreases in austral spring. Production is largely within the mixed layer, and depth-integrated phytoplankton biomass and depth-integrated NPP follow surface phytoplankton biomass. Model estimates of depth-integrated NPP based on BGC Argo float profiles are comparable with VGPM estimates for the southern water masses.

Modeling the daytime net primary productivity of maize at the canopy scale based on UAV multispectral imagery and machine learning

Net primary productivity (NPP) is an important index to evaluate the carbon absorption capacity in agricultural ecosystems, timely and accurate spatial–temporal variations of NPP play a significant role in guiding agricultural production. Currently, NPP observation at the canopy scale is primarily based on the chamber method. However, upscaling the spatial–temporal estimates of NPP at the canopy scale is still challenging. In this study, maize daytime NPP was measured by the chamber, and multispectral images of maize canopies were obtained via unmanned aerial vehicle (UAV) multispectral system. We explored the potential of multispectral images for estimating maize daytime NPP at the canopy scale. Four machine learning algorithms were employed to estimate daytime NPP using ground factors and vegetation indices (VIs) × photosynthetic active radiation (PAR) independently. NPP estimation models based on the gradient boosting regression (GBR), random forest (RF), and support vector regression (SVR) using ground factors outperformed the VIs × PAR-based model, barring the ridge regression (RR) model. Among them, the GBR-based models performed better, and the model using ground factors (R2 = 0.958) outperformed those using VIs × PAR (R2 = 0.899). However, the ground factor-based GBR model was complex and contained more input parameters, thus making it highly-time and labor-intensive, and highly destructive. Moreover, ground factor-based GBR model could not reflect the variation of NPP in maize canopy. The VIs × PAR-based GBR model could explain 89.9% of the daytime NPP, and it only required two parameters, including VIs and PAR. Therefore, the VIs × PAR-based GBR model can easily obtain high resolution and long time-series NPP information nondestructively for a large range. This study reveals that maize daytime NPP can be estimated using high-resolution UAV multispectral images and provides a good reference for promoting the monitoring and upscaling of NPP observations.

Recent nutrient enrichment and high biological productivity in the Labrador Sea is tied to enhanced winter convection

The Labrador Sea is known for strong surface heat losses and deep ocean ventilation in the wintertime as well as high biological productivity and carbon export associated with extensive spring blooms. Using satellite-derived estimates of chlorophyll-a and net primary productivity, this study documents that the Labrador Sea has recently become more productive, evident in the form of more intense and spatially extensive phytoplankton blooms. The spatial and temporal variability in chlorophyll-a was compared against trends in nutrient concentrations across the Labrador Sea. Nutrient concentrations were obtained on a repeat hydrography line running from the Labrador to Greenland shelves. The 25-year long time series of nitrate, phosphate and silicate concentrations along the AR7 W line reveal decadal trends in the upper Labrador Sea. A comparison of these nutrients with basin-averaged annual mean surface chlorophyll-a concentrations reveals positive correlations in the upper 1000 m of the Labrador Sea. Furthermore, nutrient concentrations in the upper 1000 m were strongly correlated with wintertime convection depth. During years of strong winter convection, more nutrients are entrained from the deeper Labrador Sea, which then can be distributed through the Western Greenland and Irminger Currents and eddy activity, allowing sustained phytoplankton growth further north in the Labrador Sea.

Estimation of Terrestrial Net Primary Productivity in the Yellow River Basin of China Using Light Use Efficiency Model

The net primary productivity (NPP) of vegetation is an essential factor of ecosystem functions, including the biological geochemical carbon cycle, which is often impacted by climate change and human activities. It plays a significant role in comprehending the nature of carbon balance in an ecosystem and demonstrates the global and regional carbon cycle dynamics. The present study used an upgraded CASA model to calculate the NPP in the Yellow River Basin (YRB), China. The model’s simulation ability was improved by changing the model parameters. Further, the CASA model was validated by comparing with MODIS-NPP and in situ observed NPP, wherein the accuracy of the CASA model estimation was found satisfactory to estimate NPP changes in the study area. The simulated results of the improved CASA model showed that the mean annual NPP value of vegetation in the YRB was 283.4 gC m–2 a–1 from 2001 to 2020, with a declining trend in spatial distribution from south to north. In contrast, the NPP appeared as an increasing trend in the YRB temporally from 212 gC m–2 a–1 in 2001 to 342 gC m–2 a–1 in 2020, with a mean annual growth rate of 4.6 gC m–2 a–1. The total NPP in the YRB increased by 40,088.3 GgC between 2001 and 2020, from 226.06 TgC to 266.15 TgC. This rise can be attributed to the increase in forests. The average grassland area has reduced by 4651 km2 during the last two decades, significantly impacting the total NPP of grasslands. Although the increase in NPP in wetlands was minimal, accounting for 815.53 GgC, the highest change percentage of 79.78%, could be observed among the six vegetation types due to the anthropogenic influences and climate change. The conditions favorable for vegetation growth and a sustained environment were enhanced by the increased precipitation and temperature and the reinforced ecological protection by the government.

Estimation of Terrestrial Net Primary Productivity in China from Fengyun-3D Satellite Data

Estimation of Terrestrial Net Primary Productivity in China from Fengyun-3D Satellite Data

Estimating estuarine primary production using satellite data and machine learning

• First attempt to use machine learning to estimate estuarine NPP from satellite data. • Machine learning algorithm greatly outperformed VGPM for NPP estimation. • Tampa Bay NPP shows seasonality and an increasing trend from 2003 to 2020. • Seasonality and long-term increasing trend are largely driven by water temperature. • NPP shows a spatial pattern that likely reflects differences in river discharge. Net primary production (NPP) plays an important role in estuarine carbon cycling, which has been increasingly impacted by human activities and global climate change. Spatiotemporal trends of NPP in the open ocean have been well studied using satellite data and standard primary production algorithms such as the Vertically Generalized Production Model (VGPM), but these algorithms are generally not suitable for estuarine and coastal waters. Previous remote sensing studies on estuarine NPP mainly focused on the tuning of the standard VGPM based on extensive local in situ data. Here we make a first attempt to use machine learning algorithms to estimate NPP in an estuarine environment from satellite measurements. Tampa Bay, the largest estuary in Florida (United States), has abundant in situ measurements of NPP, although the spatiotemporal variability of NPP within the bay remains unrevealed. Combining these data with concurrent MODIS/Aqua image data, we developed and evaluated seven machine learning algorithms (support vector regression, random forest, decision tree, bagging, adaptive boosting, gradient tree boosting, and neural network), and applied the one with the least root mean square error (RMSE) and highest correlation coefficient to establish a time-series NPP record for Tampa Bay from 2002 to 2020. Results of the best performing algorithm (Pearson’s r = 0.82, RMSE = 151.0 mgC m −2 d −1 ) showed substantial improvement over the standard or tuned VGPM (Pearson’s r = 0.51, RMSE = 364.9 mgC m −2 d −1 ), using the same independent variables for NPP ranging from 157.5 to 1368.8 mgC m −2 d −1 . MODIS NPP shows temporal variations that are largely driven by temperature: lowest values in winter, highest values in summer, and an increasing trend from 2003 to 2020, highlighting the impact of global warming on estuarine NPP. The spatial distribution of MODIS NPP shows higher values in Hillsborough Bay, Middle Tampa Bay and Lower Tampa Bay, and relatively lower values in Old Tampa Bay, a pattern that likely reflects differences in river discharge. The long-term NPP product derived from machine learning algorithms and satellite data can complement existing field-based monitoring programs and help to understand estuarine responses to climate changes and human impacts, and design relevant mitigation strategies.

Plant Proxy Evidence for High Rainfall and Productivity in the Eocene of Australia

AbstractDuring the early to middle Eocene, a mid‐to‐high latitudinal position and enhanced hydrological cycle in Australia would have contributed to a wetter and “greener” Australian continent where today arid to semi‐arid climates dominate. Here, we revisit 12 southern Australian plant megafossil sites from the early to middle Eocene to generate temperature, precipitation, and seasonality paleoclimate estimates, net primary productivity (NPP), and vegetation type, based on paleobotanical proxies and compare them to early Eocene global climate models. Temperature reconstructions are uniformly subtropical (mean annual, summer, and winter mean temperatures 19–21°C, 25–27°C, and 14–16°C, respectively), indicating that southern Australia was ∼5°C warmer than today, despite a >20° poleward shift from its modern geographic location. Precipitation was less homogeneous than temperature, with mean annual precipitation of ∼60 cm over inland sites and >100 cm over coastal sites. Precipitation may have been seasonal with the driest month receiving 2–7× less than the mean monthly precipitation. Proxy‐model comparison is favorable with a 1,680 ppm CO2 concentration. However, individual proxy reconstructions can disagree with models as well as with each other. In particular, seasonality reconstructions have systemic offsets. NPP estimates were higher than modern, implying a more homogenously “green” southern Australia in the early to middle Eocene when this part of Australia was at 48–64°S and larger carbon fluxes to and from the Australian biosphere. The most similar modern vegetation type is modern‐day eastern Australian subtropical forest, although the distance from coast and latitude may have led to vegetation heterogeneity.

Estimation of the net primary productivity of winter wheat based on the near-infrared radiance of vegetation

Quantifying net primary productivity (NPP) is important for understanding the global carbon cycle and for assessing ecosystem carbon dynamics. However, uncertainties remain in NPP estimation. Using winter wheat data obtained from an experimental station in 2019, this study evaluated the ability of the near-infrared radiance of vegetation (NIRV,Rad) to estimate NPP at different time scales and established an estimation model based on NIRV,Rad, where NIRV,Rad was defined as the product of the normalized difference vegetation index (NDVI) and the near-infrared radiance. The results showed that the linear relationship between NIRV,Rad and NPP was superior to the relationship between NPP and NDVI, enhanced vegetation index-2 (EVI2), and near-infrared reflectance of vegetation (NIRV,Ref) at each time scale (hourly, daily, and growth period). The advantage of NIRV,Rad was more evident on the hourly scale, in which the R2 of NIRV,Rad and NPP reached 0.77, whereas the R2 values of the correlation of NDVI, EVI2, and NIRV,Ref with NPP were 0.30, 0.16, and 0.14, respectively. There existed a strong linear relationship between absorbed photosynthetically active radiation, net photosynthetic rate, leaf area index, and NIRV,Rad, which explained the good relationship between NIRV,Rad and NPP. Through a comparative analysis of the various models, the NIRV,Rad model was found to have the strongest ability to estimate NPP and the R2, with the measured NPP reaching 0.81. The accuracy of NIRV,Rad provides a new method for estimating NPP and a scientific basis for estimating NPP using high-resolution satellite remote sensing data on a regional scale.

On carbon burial and net primary production through Earth's history

The carbonate carbon isotope record has been traditionally interpreted as evidence of stability in the globally integrated ratio of organic to total carbon burial from Earth9s surface environments over the past ∼3.8 billion years, but recent work has begun to question this conclusion. Herein, we use a reactive-transport modeling approach to track organic carbon oxidation at varying atmospheric oxygen levels and use that information to provide a rough estimate of net primary production through time. Our results support the emerging view that there was extensive variability in the fraction of carbon buried as organic matter (<i>f</i>_b,org) throughout Earth9s history. We strengthen the case that the carbonate carbon isotope record has been characterized by a relatively constant baseline value over time due to a fundamental mechanistic link between atmospheric O₂ levels and the carbon isotope composition of net inputs to the ocean-atmosphere system. Further, using estimates of the organic carbon burial flux (F_b,org) and the burial efficiency of the carbon pump from our marine reactive-transport modeling, we also support previous work suggesting extensive fluctuation in marine net primary production over time.

Operational framework to predict field level crop biomass using remote sensing and data driven models

Remote Sensing (RS) based monitoring provides opportunities to acquire timely and reliable information on crop growth at diverse scales. Crop yield forecasts can help decision makers to formulate policies on maintaining national food reserves, sustaining food supply chains and attaining national food security. Inter field crop yield heterogeneity arising from varied field management and agricultural practices necessitates this forecasting to be done at field-level. Such field level information can aid farmers to identify gaps in water and farm management practices and take corrective actions, if needed. So far, no scalable tools are available to predict crop yields at field level that leverage the availability of open-access high-resolution RS data.This research implements an operational framework to predict field level crop biomass by evaluating different regression algorithms to develop data driven models, leveraging historical and near real time open-access high resolution optical satellite data from Sentinel-2, radar data from Sentinel-1 and evapotranspiration (ETa) and Net Primary Production (NPP) data from Food and Agriculture Organization’s (FAO) Water Productivity through Open-access Remotely sensed data (WaPOR) platform. NPP is used as a proxy for biomass production/yield. Five of the most commonly used regression algorithms were tested to build a data-driven model for sugarcane NPP prediction in Wonji-Shoa sugarcane estate, located in the Awash Basin, Ethiopia. The models tested were the Multivariate Linear Regression (MLR), Stepwise Multivariate Linear Regression (SMLR), Boosted Regression Trees (BRT), Support Vector Regression (SVR), and Random Forest Regression (RFR).The results revealed that for seasonal sugarcane NPP predictions, the linear regression models (MLR and SMLR) yielded more accurate predictions than the non-linear machine learning models (BRT, SVR and RFR) tested. The highest accuracy was achieved for MLR models for which estimates with 89% accuracy could be made 4 months prior to the harvest and with accuracies of 79% up to 200 days (approx. 6.5 months) before the harvest. The non-linear machine learning models, however, could not provide reliable estimates of sugarcane NPP (accuracies < 61%). Cumulative vegetation indices (VIs) were found to have higher predictive power than standard VIs for predicting future sugarcane NPP. Cumulative Enhanced Vegetation Index (EVI) was found to be the variable with the highest predictive power, followed by VH polarized Sentinel-1 Synthetic Aperture Radar data and WaPOR ETa. The study shows the usefulness of high-resolution RS information to predict seasonal NPP at field level. The methods presented here can be translated into an automated framework towards an operational system.

High spatial resolution modelling of net forest carbon fluxes based on ground and remote sensing data

This paper presents the application of a recently proposed modelling strategy to yield high spatial resolution estimates of net forest carbon fluxes in Tuscany (Central Italy). The simulation of forest net primary production (NPP) and net ecosystem production (NEP) is based on the combination of remotely sensed and ancillary data which describe the main characteristics of local environment and vegetation. Distinctively, the methodology is driven by a map of growing stock volume (GSV) having a pixel size of 23 × 23 m2 and can therefore yield correspondingly high spatial resolution estimates of forest NPP and NEP. An advancement of the original methodology is also proposed based on the availability of a recently produced soil organic carbon map of Tuscany informative about biomass decomposition (heterotrophic respiration). The modified modelling strategy is applied over the period 2001–2005 and the obtained estimates are assessed against: i) ground observations of GSV current annual increment (CAI) collected for over 600 plots during the last National Forest Inventory of Italy; ii) a high spatial resolution reference NEP map obtained for a Mediterranean pine forest (San Rossore) by the integration of eddy covariance flux data and local CAI observations. Considering the complexity of the simulated processes and of the examined environment, the estimation accuracy achieved is satisfactory for both NPP and NEP. This supports the possibility of applying the proposed modelling strategy to estimate net carbon fluxes at high spatial resolution in other forest environments.

Partitioning the Export of Distinct Biogenic Carbon Pools in the Northeast Pacific Ocean Using a Biogeochemical Profiling Float

AbstractWe leverage observations from chemical and bio‐optical sensors mounted on a biogeochemical profiling float in the Northeast Pacific Ocean to quantify the cycling and export potential of distinct biogenic carbon pools, including particulate inorganic carbon (PIC), particulate organic carbon (POC), and dissolved organic carbon (DOC). Year‐round observations reveal complex carbon cycle dynamics among these carbon pools. Net DOC production peaked during bloom initiation, about 3 months prior to the summer peak in POC production. We validate the float estimates of DOC cycling with seasonal accumulation and removal rates derived from ship‐board DOC observations over the same period. By combining chemical and bio‐optical tracers of POC cycling, we estimate the instantaneous POC sinking flux (). The cooccurrence of DOC consumption and POC production and sinking during fall and winter resolves the regional conundrum of a persistent particle sinking flux observed by sediment traps during a season that is known to be heterotrophic. PIC production is small, and uncertainties are large. By combining float‐based estimates of instantaneous net primary production (NPP) and , we quantify a real‐time carbon export ratio ([/NPP] × 100%) for the euphotic zone. Elevated export ratios during summer are associated with an increase in the fraction of particles larger than 100 μm in size. Elevated export ratios during winter are associated with the physical redistribution of particles through seasonal deep mixing. Our study demonstrates how the combined use of multiple sensors on biogeochemical profiling floats can provide more nuanced information about upper ocean carbon cycle dynamics.

Analysis and Estimation of Net Primary Productivity of Vegetation in Nanjing Using Multi-Sourced Remote Sensing Data

Net primary productivity (NPP) is an important indicator of Earth’s ecosystem. However, NPP estimation of cities by high spatial resolution is limited in China, especially Nanjing, and NPP evolution and quantitative analysis of influencing factors in time series are insufficient. Nanjing, as one of the central cities in China, plays a major role in China’s economic development. In this paper, high spatial resolution data combined with Landsat Thematic Mapper (TM) data are used for NPP estimation and analysis in Nanjing City. The research contents of this paper are as follows: (1) To improve the passive effect of “red border region” on GF-1 WFV forest and farmland classification ability, the multispectral and high-resolution images are fused, and the image fusion effect was also evaluated. (2) The NPP of four seasons in Nanjing in 2017 was estimated using the CASA model. (3) The results of NPP estimation are analyzed based on statistical principles. The advantage of this paper lies in the comprehensive utilization of multi-source data and the quantitative analysis of the response mechanism between meteorological data and the NPP estimation value.

中国陆地植被净初级生产力估算模型优化与分析——基于中国生态系统研究网络数据

PDF HTML阅读 XML下载 导出引用 引用提醒 中国陆地植被净初级生产力估算模型优化与分析——基于中国生态系统研究网络数据 DOI: 10.5846/stxb202011263031 作者: 作者单位: 作者简介: 通讯作者: 中图分类号: 基金项目: 国家重点研发计划项目（2016YFC0500201，2016YFC0502102-02）和国家自然科学基金（41771464，31761143018-2） Optimization of net primary productivity estimation model for terrestrial vegetation in China based on CERN data Author: Affiliation: Fund Project: 摘要 | 图/表 | 访问统计 | 参考文献 | 相似文献 | 引证文献 | 资源附件 | 文章评论 摘要:该研究基于中国生态系统研究网络（CERN）数据对传统CASA模型进行优化，对比两叶模型与优化CASA模型在站点尺度和像元尺度对于8个典型生态站点的植被净初级生产力（NPP）估算精度，选择在像元尺度表现更好的优化CASA模型，结合中国土地覆被数据（ChinaCover）开展2000-2019年中国陆地植被NPP监测与分析。研究结果表明：（1）基于FY2D PAR的优化方案能够有效避免空间插值导致的不确定性问题，显著提高了PAR估算精度；（2）在站点尺度上，两叶模型用于估算典型森林、草地生态系统的NPP表现更好，而在像元尺度上优化CASA模型估算精度更高；（3）在全国尺度上，优化了最大光能利用率、水分胁迫系数以及光合有效辐射计算方法的CASA模型能够较好地模拟中国陆地植被NPP，近20年中国陆地植被NPP变化范围为2.703-2.882 PgC/a，在空间上呈西北低东南高的格局，在时间上呈现波动中缓慢增加的趋势。 Abstract:In this study, we optimized the traditional Carnegie-Ames-Stanford Approach (CASA) model based on China Ecosystem Research Network (CERN) datasets, and compared the estimation accuracy of the two-leaf model and the optimized CASA model at the site scale and pixel scale at eight CERN sites covering major ecosystem types. The optimized CASA model with better performance at the pixel scale combined with China Land Cover Data (ChinaCover) were employed for mapping and monitoring the spatio-temporal changes of terrestrial vegetation net primary production (NPP) in China from 2000 to 2019. The results show that:(1) the optimization for model input parameter of photosynthetically active radiation based on FY2D PAR can effectively avoid the uncertainty caused by the spatial interpolation, and significantly improve the accuracy of PAR estimation; (2) The two-leaf model shows higher NPP estimation accuracy at the site scale, while the optimized CASA model performs better for the NPP estimation at the pixel scale; (3) At the national scale, the CASA model with optimized maximum light energy use efficiency, water stress coefficient and photosynthetically active radiation can better simulate China's terrestrial vegetation NPP. The estimated total NPP in Chinese terrestrial vegetation ranges from 2.703 PgC/a to 2.882 PgC/a in the past 20 years and indicates a fluctuated and slow increasing trend. The spatial distribution of the NPP in China shows a general pattern of gradually increasing from northwest to southeast. 参考文献 相似文献 引证文献

Estimation and Spatiotemporal Variation Analysis of Net Primary Productivity in the Upper Luanhe River Basin in China From 2001 to 2017 Combining With a Downscaling Method

The upper Luanhe River Basin is a significant ecological barrier guarding the Beijing–Tianjin–Hebei region in China. Quantitative measures of vegetation productivity can be used to assess ecosystem carbon sequestration capacity and monitor regional ecological environmental health. Although several vegetation productivity products have been generated, poor spatiotemporal resolution limits their application in ecosystem service assessment. In this article, vegetation net primary productivity (NPP) from 2000 to 2017 with a resolution of 30 m in the upper Luanhe River Basin was generated based on a data fusion model and the multisource data synergized quantitative (MuSyQ) NPP model. Then, the variation trend of NPP and its climate controls were analyzed. Compared with forest NPP observation data, we derived an <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">R</i> <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> of 0.68 and the root-mean-square error of 81.70 gC <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">.</sup> m <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">−2.</sup> yr <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">−1</sup> . Annual NPP had a fluctuating increasing trend from 2001 to 2017, with values ranging between 3.43 and 5.00 TgC <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">.</sup> yr <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">−1</sup> , with an annual increase trend of 0.04 TgC <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">.</sup> yr <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">−1</sup> . Precipitation was significantly correlated with NPP in the upper part of the Luanhe River basin, which is an important reason for the interannual variation of NPP. Grassland had a stronger correlation to precipitation than forest because it is more sensitive to precipitation. The area where the temperature is significantly correlated with annual NPP only accounts for 2% of the study area, indicating that temperature has a weak influence on NPP. Furthermore, human activities, such as forest management, fertilization, and irrigation, can change the trend of annual NPP.

Evaluation and Validation of the Net Primary Productivity of the Zoigê Wetland Based on Grazing Coupled Remote Sensing Process Model

Zoigê wetland is located in the southeastern edge of the Qinghai–Tibet Plateau, in recent years, the wetland local area grassland productivity decline, land sanding, and other serious degradation phenomenon. Using remote sensing and hydrology data combined with the basic principles of vegetation ecology, we applied the Carnegie–Ames–Stanford Approach (CASA) model and Boreal Ecosystem Productivity Simulator (BEPS) model to invert the net primary productivity (NPP) of the Zoigê wetland ecosystem and then identified the deficiencies of the two models. In this article, we tested the factors that influence the light use efficiency of vegetation and considered the effects of livestock grazing on grassland ecosystems and wetland moisture content on vegetation growth. A new grazing coupled remote sensing process (GCRSP) model suitable for the productivity of the Zoigê wetland was proposed. Field survey data were combined with the CASA model, BEPS model, and Moderate Resolution Imaging Spectroradiometer (MODIS) NPP product, and the accuracy of the proposed GCRSP model was analyzed at regional level. The results show that after fitting the field measurements to the GCRSP model, the fitted variance between the model and the measured data is 0.84, which is larger than the variance between the other two methods and the measured data. In addition, the variance of the linear fit between the GCRSP model and the MODIS NPP product was 0.11, which was relatively small, indicating that the regional-scale GCRSP model was relatively accurate for the Zoigê wetland NPP. Therefore, this model was suitable for the inversion of regional-scale NPP. The results indicated that the average grassland NPP of the Zoigê wetland was 487.57 gC/m <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> , and larger NPP values were distributed near the rivers and wetland and smaller NPP values were distributed in the mountain glacier and desertified land areas. These results were consistent with the regional characteristics. This article provided technical and theoretical support for the desertification management of the Zoigê grassland and a new method for studying vegetation NPP, and the findings could help improve the accuracy of regional-scale vegetation NPP estimates.

The mangrove blue carbon sink potential: Evidence from three net primary production assessment methods

Mangrove forests are among the highest carbon (C) sinks in the tropics and subtropics worldwide. Net primary productivity (NPP) is estimated by the permanent plot method with litterfall plus incremental growth (LG) and has great significance for the evaluation of the carbon sequestration capacity of forests. Here, we developed a new high-resolution method, sap flow investigation (SF), for the evaluation of carbon sequestration capacity. We also compared the forest carbon sequestration capacity estimated using two NPP estimation methods, stand water use using SF and light attenuation/gas exchange (LA), with traditional permanent plots using the LG method in four typical mangrove forests in the Futian Mangrove Reserve of Shenzhen City, China. The LG results showed that mangrove forests had NPPs ranging from 7.67 to 11.87 Mg C ha−1 yr−1, with species-specific differences. Among the three methods, the LG method is the most widely used, although it is costly and difficult to implement in the field. The LA results showed seasonal variation of NPP, with 4.69 g C m−2 d-1 in summer and 2.02 g C m−2 d-1 in winter. The LA method is relatively simple, but it requires a homogeneous canopy structure with a simple canopy extinction coefficient, which is suitable for mangroves with homogenous canopy structures. Although costly, the most convenient and efficient method is the SF method, which can realize continuous and high-precision dynamic NPP monitoring at the stand scale. After standardization by the LG method, the two high-resolution NPP methods could be used in mangrove forests with different time scales. For the global scale, after modification by the correction method provided in our study, the error caused by the determination method could be eliminated, which could make the global database more plentiful and accurate.

Field‐Based Estimation of Net Primary Productivity and Its Above‐ and Belowground Partitioning in Global Grasslands

AbstractNet primary productivity (NPP) in global grasslands is a critical component of terrestrial carbon cycling and the primary source of food for herbivores. However, the size and spatial distribution of NPP across global grasslands remain unclear, especially for belowground NPP (BNPP), which limits our understanding of above‐ and belowground carbon cycling and the assessments of herbivore food security. Here, we compiled a comprehensive grassland NPP database with 1,467 field measurements to estimate the spatial distributions of aboveground NPP (ANPP), BNPP, total NPP (TNPP), and the fraction of BNPP (fBNPP) using the random forest (RF) model. The global mean grassland ANPP, BNPP, TNPP, and fBNPP were 433 ± 31 g m−2 yr−1, 593 ± 47 g m−2 yr−1, 979 ± 78 g m−2 yr−1, and 0.54 ± 0.02, respectively. The total ANPP, BNPP, and TNPP over global grasslands were 6.84 ± 0.49, 9.36 ± 0.74, and 15.46 ± 1.23 Pg C yr−1, respectively. ANPP, BNPP, and TNPP exhibited decreasing trends from low latitudes toward the poles. The spatial pattern of fBNPP was almost opposite to that of ANPP. Climate was a major determinant in shaping the spatial distribution of ANPP and TNPP, while soil and vegetation had significant impacts on that of BNPP and fBNPP. Our findings suggest that field data‐driven estimation of NPP using the RF model could be a useful approach for obtaining spatially explicit NPP of grasslands, particularly BNPP products, and that more attention should be paid on belowground and non‐climatic factors to better assess the carbon cycle in global grasslands.

In situ estimates of kelp forest productivity in macro‐tidal environments

AbstractAccurately quantifying primary productivity of marine macrophytes is essential to further understanding of coastal ecosystem functioning and will allow its inclusion within marine ecosystem models. Traditional quantification of carbon standing stock and biomass accumulation only allow rough estimates of net primary production whereas incubations that measure photosynthesis in the laboratory lack realism and underestimate true photosynthetic rates. We developed in situ photorespirometry methods to estimate productivity of Laminaria hyperborea, the dominant forest‐building kelp species in Northeast Atlantic. We show that sub‐canopy individuals are better adapted to low light levels with higher photosynthetic efficiency and lower compensation point than larger canopy individuals. Laboratory incubations of L. hyperborea stipes revealed that productivity rates could be positive or negative, even at saturating irradiances, depending on whether they were dominated by autotrophic or heterotrophic communities, respectively. Scaling up productivity estimates based on local population demographics resulted in mean daily net productivity rates of 13.3 and −30.1 g C m−2 d−1, for shallow L. hyperborea forest during late summer and autumn, respectively. At saturating irradiances in the late summer–autumn study period, estimates of net and gross productivity reached 9.4 and 12 g C m−2 h−1, respectively. Sub‐canopy individuals were responsible for 12% of estimated daily productivity during summer, but 20% in autumn. We conclude that in situ photorespirometry provides a more direct measurement of production, unaffected by exudation of dissolved carbon or tissue loss, that can account for sub‐canopy and stipe components of the kelp forest, commonly excluded from traditional studies.