Vegetation Photosynthesis Model Research Articles

Temporal and spatial variations and influencing factors of gross primary productivity in Changbai Mountain Nature Reserve, China.

Changbai Mountain Nature Reserve (CNR) is a typical temperate forest ecosystem, and gross primary production (GPP) of which is closely related to topography and climate change. Research on the spatio-temporal variations and influencing factors of GPP in the CNR is of great significance for assessing growth status of vegetation and the quality of ecological environment. We calculated GPP in CNR using the vegetation photosynthesis model (VPM), and analyzed the influences of slope, altitude, temperature, precipitation, and total radiation. The results showed that the range of annual average GPP in CNR was 63-1706 g C·m-2·a-1 from 2000 to 2020 and that GPP decreased with the increases of altitude. Temperature played the most important role in driving the spatial varia-tion of GPP, with a significant positive correlation with GPP. During the study period, the overall annual GPP showed a significant increase trend in CNR, with an average annual increase of 13 g C·m-2·a-1. The areas with increase of annual GPP accounted for 79.9% of the total area, and the area proportion of annual GPP increase differed in each plant functional type. Annual precipitation was significantly negatively correlated with GPP in 43.2% of CNR, while annual mean temperature and annual total radiation were significantly positively correlated with GPP in 47.2% and 82.4% of CNR. GPP would increase continuously in CNR under the scenario of future global warming.

Improving the Estimation of Gross Primary Productivity across Global Biomes by Modeling Light Use Efficiency through Machine Learning

Estimating gross primary productivity (GPP) is important for simulating the subsequent carbon cycle elements and assessing the capacity of terrestrial ecosystems to support the sustainable development of human society. Light use efficiency (LUE) models were widely used to estimate GPP due to their concise model structures. However, quantifying LUEmax (maximum light use efficiency) and representing the responses of photosynthesis to environmental factors are still subject to large uncertainties, which lead to substantial errors in GPP simulations. In this study, we developed a hybrid model based on machine learning and a LUE model for GPP estimates. This hybrid model was built by targeting LUE with a machine learning approach, namely multi-layer perceptron (MLP), and then, estimating GPP within a LUE model framework with the MLP-based LUE and other required inputs. We trained the hybrid LUE (H-LUE) model and then, compared it against two conventional LUE models, the vegetation photosynthesis model (VPM) and vegetation photosynthesis and respiration model (VPRM), regarding GPP estimation, using tower-based daily-scale observations from 180 flux sites that cover nine different plant function types (PFTs). The results revealed better performance (R2 = 0.86 and RMSE = 1.79 gC m−2 d−1 on the test dataset) of the H-LUE model compared to the VPM and VPRM. Evaluations of the three models under four different extreme conditions consistently revealed better performance of the H-LUE model, indicating greater adaptability of the model to varied environments in the context of climate change. Furthermore, we also found that the H-LUE model can reasonably represent the responses of the LUE to meteorological variables. Our study revealed the reliable and robust performance of the developed hybrid LUE when simulating GPP across global biomes, providing references for developing better hybrid GPP models.

Effect of meteorological data assimilation using 3DVAR on high-resolution simulations of atmospheric CO2 concentrations in East Asia

In this study, the effects of meteorological data assimilation (DA) on high-resolution CO2 concentration simulations in East Asia were investigated using the Weather Research and Forecasting model coupled with Chemistry (WRF-Chem) and three-dimensional variational data assimilation (3DVAR). Experiments with and without meteorological DA were performed for June and December 2018. To determine an appropriate DA cycling interval, two experiments were performed at cycling intervals of 6 and 12 h. Compared to the meteorological fields simulated from experiment without DA, those simulated from experiments with DA were more similar to atmospheric observations. The CO2 concentrations simulated from the experiment with a 6 h DA cycling interval were highly similar to the observed CO2 concentrations. Compared to the root mean square error (RMSE) of the experiment without DA, the RMSE of simulated CO2 concentrations in the experiment with a 6 h DA interval decreased by 8.19% in June and 1.43% in December with respect to surface CO2 concentration observations, and decreased by 2.52% in June and 0.47% in December against column-averaged CO2 concentration (XCO2) observations of the Orbiting Carbon Observatory 2 (OCO-2). The improved 2 m temperature forecasts by DA enhanced biogenic CO2 simulations using the Vegetation Photosynthesis and Respiration Model (VPRM) in WRF-Chem. The improved wind speed and direction forecasts by DA led to improved simulations of the horizontal transport of CO2 concentrations and planetary boundary layer height. Overall, enhanced meteorological forecasts by meteorological DA using 3DVAR improved the accuracy of high-resolution CO2 concentration simulations using WRF-Chem in East Asia.

Very high-resolution Net Ecosystem Exchange over India using Vegetation Photosynthesis and Respiration Model (VPRM) simulations

The terrestrial biosphere has a crucial role in controlling the rate of CO2 accumulation in the atmosphere. The quantification of Net Ecosystem Exchange (NEE) is critical to determine whether the regional terrestrial ecosystem is a net sink or source of CO2. This study uses a satellite-data derived light-use-efficiency model (the Vegetation Photosynthesis and Respiration Model, VPRM) to compute the biospheric CO2 fluxes over India from 2011–2020. A very high-resolution Land Use-Land Cover (LULC) data from the IRS-P6 satellite and MODIS derived surface reflectance are utilised to compute the NEE over distinct vegetation types at ∼9km× 9 km. The VPRM model for the Indian region captures the spatial pattern and seasonal features of NEE over the country. The magnitude of annual mean NEE over India from the VPRM model is relatively high compared to that from Carbon Tracker, FLUXCOM, and Soil Moisture Active Passive Level-4 carbon data products. However, the VPRM model simulated NEE agrees with the NEE observations from the eddy covariance estimates, which are accepted as the ground truth. The fusion of high-resolution LULC and surface reflectance data in VPRM suggests that the average NEE over the Indian region during 2011–2020 is −0.16 ± 0.02 PgC yr−1. Among this, the contribution from Gross Ecosystem Exchange is about −0.47 ± 0.02 PgC yr−1, and Respiration is 0.31 ± 0.01 PgC yr−1. The maximum biospheric CO2 absorption is during the post-monsoon season, and the minimum is during the pre-monsoon season. The very high spatial resolution and heterogeneity resolved in the NEE data derived in this work offer the first data product of this kind for a variety of NEE analyses over India.

Terrestrial CO2 exchange diagnosis using a peatland-optimized vegetation photosynthesis and respiration model (VPRM) for the Hudson Bay Lowlands

Satellite-based light use efficiency (LUE) models have been widely used to estimate gross primary production in various terrestrial ecosystems such as forests and croplands, but northern peatlands have received less attention. In particular, the Hudson Bay Lowlands (HBL) which is a massive peatland-rich region in Canada has been largely ignored in previous LUE-based studies. These peatland ecosystems have accumulated large stocks of organic carbon over many millennia, and play a vital role in the global carbon cycle. In this study, we used the satellite data-driven Vegetation Photosynthesis and Respiration Model (VPRM) to examine the suitability of LUE models for carbon flux diagnosis in the HBL. VPRM was driven alternately with the satellite-derived enhanced vegetation index (EVI) and solar-induced chlorophyll fluorescence (SIF). The model parameter values were constrained by eddy covariance (EC) tower observations from the Churchill fen and Attawapiskat River bog sites. The main objectives of the study were to (i) investigate if site-specific parameter optimization improved NEE estimates, (ii) determine which satellite-based proxy of photosynthesis produced more reliable estimates of peatland net carbon exchange, and (iii) examine how LUE and other model parameters vary within and between the study sites. The results indicate that the VPRM mean diurnal and monthly estimates of NEE had significant strong agreements with EC tower fluxes at the two study sites. A comparison of the site-optimized VPRM against a generic peatland-optimized version of the model revealed that the site-optimized VPRM provided better estimates of NEE only during the calibration period at the Churchill fen. The diurnal and seasonal cycles of peatland carbon exchange were better captured by the SIF-driven VPRM, demonstrating that SIF is a more accurate proxy for photosynthesis compared to EVI. Our study suggests that satellite-based LUE models have the potential to be applied on a larger scale to the HBL region.

Estimating the gross primary productivity based on VPM correction model for Xishuangbanna tropical seasonal rainforest

ABSTRACT Due to the unique climatic characteristics and vegetation features of tropical regions, the correlation R2 of gross primary productivity (GPP) estimation in tropical regions using remote sensing models was generally lower than 0.3. Therefore, for the cloudy and rainy tropical regions, the influence brought by clouds on remote sensing images needed to be considered in GPP estimation. This paper developed a corrected vegetation photosynthesis model (VPM) for estimating GPP under cloudy conditions. It mainly corrected the two parameters, W scalar and Enhanced Vegetation Index (EVI), which were obtained from remote sensing images and were therefore greatly influenced by clouds in the model. First, the water stress factor W scalar was replaced by Evaporation Fraction (EF). Secondly, using the good correlation between near surface temperature and EVI, the conversion coefficient between near surface temperature and EVI was fitted to achieve the effective reconstruction of EVI contaminated by clouds. The correction of the two factors improved the estimation accuracy of the VPM model, and the comparison with the observed values of the GPP site in 4 years showed that the correction of EVI had a better improvement, with an increase of 0.22 in R2 compared with the pre-correction, and the correction of W scalar was increased by 0.11 in R2. To verify the proposed method, the in-situ observation data of Xishuangbanna flux site from 2007 to 2010 were used. The results showed that the proposed method effectively improved the accuracy of GPP estimation by VPM model, especially in 2007 it was strongly influenced by clouds, and the improvement was significant, with R2 increasing from 0.2 to 0.82. In general, the accuracy of GPP estimation by the proposed method had been significantly improved, with RMSE (gC·m−2·8 day−1) decreasing from 15,14.4, 18.1, 14.2 to 8.07, 6.56, 10.33, 11.44, respectively. Therefore, the proposed method can be used to estimate the GPP for tropical seasonal rain forests in Xishuangbanna.

Net Primary Productivity Estimation Using a Modified MOD17A3 Model in the Three-River Headwaters Region

Remote sensing (RS) models can easily estimate the net primary productivity (NPP) on a large scale. The majority of RS models try to couple the effects of temperature, water, stand age, and CO2 concentration to attenuate the maximum light use efficiency (LUE) in the NPP models. The water effect is considered the most unpredictable, significant, and challenging. Because the stomata of alpine plants are less sensitive to limiting water vapor loss, the typically employed atmospheric moisture deficit or canopy water content may be less sensitive in signaling water stress on plant photosynthesis. This study introduces a soil moisture (SM) content index and an alpine vegetation photosynthesis model (AVPM) to quantify the RS NPP for the alpine ecosystem over the Three-River Headwaters (TRH) region. The SM content index was based on the minimum relative humidity and maximum vapor pressure deficit during the noon, and the AVPM model was based on the framework of a moderate resolution imaging spectroradiometer NPP (MOD17) model. A case study was conducted in the TRH region, covering an area of approximately 36.3 × 104 km2. The results demonstrated that the AVPM NPP greatly outperformed the MOD17 and had superior accuracy. Compared with the MOD17, the average bias of the AVPM was −9.8 gCm−2yr−1, which was reduced by 91.8%. The average mean absolute percent error was 57.0%, which was reduced by 68.2%. The average Pearson’s correlation coefficient was 0.4809, which was improved by 30.0%. The improvements in the NPP estimation were mainly attributed to the decreasing estimation of the water stress coefficient on the NPP, which was considered the higher constraint of water impact on plant photosynthesis. Therefore, the AVPM model is more accurate in estimating the NPP for the alpine ecosystem. This is of great significance for accurately assessing the vegetation growth of alpine ecosystems across the entire Qinghai–Tibet Plateau in the context of grassland degradation and black soil beach management.

Accounting for Carbon Sink and Its Dominant Influencing Factors in Chinese Ecological Space

Ecological space (ES), including forest ecological space (FES) and grassland ecological space (GES) in this study, is the land with natural attributes and the main functions of providing ecological services, which has a huge potential capacity for carbon sink (CS). The interannual fluctuation of the CS in ES is severe, which is affected by factors such as precipitation and temperature, but it is still controversial which is the dominant factor in affecting the fluctuation process of the CS in ES. To this end, the multi-source remote sensing monitoring data on the fine-grid scale were collected in this study, including the land use and land cover remote sensing monitoring data, the data products of moderate-resolution imaging spectroradiometer (including land surface water index, photosynthetically active radiation, enhanced vegetation index, gross primary productivity), and meteorological data (including precipitation and temperature). By coupling the vegetation photosynthesis model and soil respiration model, the CS in CES from 2010 to 2020 was calculated, and the interannual fluctuation trends and stability of CS in CES were analyzed. Furthermore, the correlation coefficient and partial correlation coefficient equation between the CS of CES with precipitation and temperature were constructed to explore the correlation between interannual fluctuation of CS in CES with meteorological factor, and to determine the dominant position of precipitation and temperature in affecting the fluctuation process of the CS in CES. The research results show that the annual average CS of per unit area in CES was 233.78 gC·m−2·a−1, and the cumulative CS was 11.83 PgC. The GES and FES contributed 6.33 PgC and 5.49 PgC of CS, respectively. From 2010 to 2020, the CS of CES showed an upward trend and was generally in a relatively stable state (the mean value of the coefficient of variation was 0.6248). However, the year with severe fluctuation of was found in this study (from 2013 to 2015), the reason is that the precipitation was too low in 2014, which indicated that climate change, especially the change of precipitation, played a important role in the fluctuation of CS in CES. The results of correlation analysis confirmed the above analysis. The change of CS in CES is highly positively correlated with the change of precipitation (the correlation coefficient is 0.085), and weakly positively correlation with temperature (the correlation coefficient was 0.026). The precipitation is the dominant influencing factor, which has a positive effect on CS in CES. Within a climate environment dominated by precipitation, precipitation and temperature jointly affect the CS in CES. It should be noted that in some regions with variable climate, precipitation and temperature had relatively little impact on CS in CES. Their fluctuations may depend more on the ecosystem’s own ecological services’ regulation ability and their response degree to changes in atmospheric CO2 concentration.

Isolating the direct and indirect impacts of urbanization on vegetation carbon sequestration capacity in a large oasis city: evidence from Urumqi, China

ABSTRACT Oasis cities are deeply affected by human activities in arid and semi-arid regions. Vegetation is an important repository in the carbon cycle of oasis urban ecosystems. The continuous expansion of urban has a disproportionate impact on the carbon sequestration capacity of vegetation. Till now, studies have been conducted to quantify the impact of urbanization on vegetation carbon sequestration capacity, the mechanism of such impact remains unclear and lacks systematic investigations, especially in oasis urban. Understanding the impact mechanism greatly benefits the sustainable development of oasis urban and regional carbon neutrality. To fill this knowledge gap, we design a theoretical framework to analyze the impact of urbanization on vegetation carbon sequestration capacity by isolating the direct and indirect impacts in Urumqi, China. Some results based on Landsat images indicated that the Impervious Surface Areas (ISAs) expanded by 436.98 km2 during 2000 – 2019. The Net Primary Productivity (NPP) calculated using the Vegetation Photosynthesis Model (VPM) was directly caused by the loss of 51.45 Gg C (1 Gg = 109 g). Another important finding proved that the direct carbon loss caused by urbanization did not change the upward trend of the total carbon sequestration capacity of vegetation, but only slowed down this upward trend by 20.86%. Our results provide new insights into urban vegetation carbon sinks and contribute to a better understanding of the uncertainty of urbanization toward carbon neutrality. This study will provide scientific data support and suggestions for the sustainable development of oasis cities and the regional carbon budget.

Analysis of regional CO2 contributions at the high Alpine observatory Jungfraujoch by means of atmospheric transport simulations and δ13C

Abstract. In this study, we investigated the regional contributions of carbon dioxide (CO2) at the location of the high Alpine observatory Jungfraujoch (JFJ, Switzerland, 3580 m a.s.l.). To this purpose, we combined receptor-oriented atmospheric transport simulations for CO2 concentration in the period 2009–2017 with stable carbon isotope (δ13C–CO2) information. We applied two Lagrangian particle dispersion models driven by output from two different numerical weather prediction systems (FLEXPART–COSMO and STILT-ECMWF) in order to simulate CO2 concentration at JFJ based on regional CO2 fluxes, to estimate atmospheric δ13C–CO2, and to obtain model-based estimates of the mixed source signatures (δ13Cm). Anthropogenic fluxes were taken from a fuel-type-specific version of the EDGAR v4.3 inventory, while ecosystem fluxes were based on the Vegetation Photosynthesis and Respiration Model (VPRM). The simulations of CO2, δ13C–CO2, and δ13Cm were then compared to observations performed by quantum cascade laser absorption spectroscopy. The models captured around 40 % of the regional CO2 variability above or below the large-scale background and up to 35 % of the regional variability in δ13C–CO2. This is according to expectations considering the complex Alpine topography, the low intensity of regional signals at JFJ, and the challenging measurements. Best agreement between simulations and observations in terms of short-term variability and intensity of the signals for CO2 and δ13C–CO2 was found between late autumn and early spring. The agreement was inferior in the early autumn periods and during summer. This may be associated with the atmospheric transport representation in the models. In addition, the net ecosystem exchange fluxes are a possible source of error, either through inaccuracies in their representation in VPRM for the (Alpine) vegetation or through a day (uptake) vs. night (respiration) transport discrimination to JFJ. Furthermore, the simulations suggest that JFJ is subject to relatively small regional anthropogenic contributions due to its remote location (elevated and far from major anthropogenic sources) and the limited planetary boundary layer influence during winter. Instead, the station is primarily exposed to summertime ecosystem CO2 contributions, which are dominated by rather nearby sources (within 100 km). Even during winter, simulated gross ecosystem respiration accounted for approximately 50 % of all contributions to the CO2 concentrations above the large-scale background. The model-based monthly mean δ13Cm ranged from − 22 ‰ in winter to − 28 ‰ in summer and reached the most depleted values of − 35 ‰ at higher fractions of natural gas combustion, as well as the most enriched values of − 17 ‰ to − 12 ‰ when impacted by cement production emissions. Observation-based δ13Cm values were derived independently from the simulations by a moving Keeling-plot approach. While model-based estimates spread in a narrow range, observation-based δ13Cm values exhibited a larger scatter and were limited to a smaller number of data points due to the stringent analysis prerequisites.

Effects of climate change and human activities on gross primary productivity in the Heihe River Basin, China.

As the primary source of carbon dioxide fixation, vegetation is critical to the carbon sink process. In this paper, the Net Primary Productivity (NPP) and the Gross Primary Productivity (GPP) were simulated using the Carnegie-Ames-Stanford Approach (CASA) model and the Vegetation Photosynthesis Model (VPM), respectively, and then the Potential Gross Primary Productivity (PGPP) and the GPP affected by human activities (AGPP) were simulated by combining Potential Net Primary Productivity (PNPP), and then the impact of climate change and human activities on GPP was assessed in the Heihe River Basin (HRB). The results showed that the GPP of grassland and Bare or Sparse Vegetation (BSV) exhibited a fluctuation rise, with increases of 0.709 gCm-2 a-1 and 0.115 gCm-2 a-1, respectively, whereas the GPP of cropland showed a fluctuation reduction, with a decline rate of -0.465 gCm-2 a-1. Climate change and human activity are both positive for vegetation growth, and human activity being the primary factor influencing GPP change. Human-dominated vegetation restoration accounted for 56.1% of the overall restoration area, with grassland GPP being the most visible response to human activities. The GPP changes in crop and grassland had a positive correlation with precipitation but a negative correlation with temperature among climate change factors, whereas the GPP changes in BSV had a negative correlation with both precipitation and temperature. Quantitative analyses of climate change and human activities' dynamic contributions to vegetation can give scientific and theoretical insight for dealing with global climate change.

TROPOMI SIF reveals large uncertainty in estimating the end of plant growing season from vegetation indices data in the Tibetan Plateau

Large-scale land surface phenology (LSP) information has been developed from remote sensing-based vegetation indices (VIs) data. However, there are considerable discrepancies and uncertainties in the LSP data products for the start and end of growing seasons (SOS; EOS) as different vegetation indices and algorithms are used. Here, we used the TROPOspheric Monitoring Instrument (TROPOMI) solar-induced chlorophyll fluorescence (SIF) data to estimate SOS and EOS in the Tibetan Plateau, a global hotspot of vegetation response to climate change. We compared SIF-based phenological metrics to those derived from VIs (e.g., normalized difference vegetation index (NDVI), enhanced vegetation index (EVI), and near-infrared reflectance of vegetation (NIRv)), and gross primary production (GPP) simulated by the vegetation photosynthesis model (VPM). We found relatively small discrepancies in SOS between SIF and VIs, but large differences in EOS. Thus, the length of the growing season derived from VIs was as much as two months longer than that estimated by SIF. These results were consistent across three products, bidirectional reflectance distribution function (BRDF) adjusted (MCD43), standard MODIS (MOD09), and TROPOMI products. The EOS discrepancy remained after excluding two mismatches (solar illumination and viewing angle) between satellite sensors. We also found that VIs-based EOS occurred below the freezing point, while SIF-based EOS occurred above the freezing point, suggesting that SIF-based EOS is more physiologically meaningful. Our study proposed that the late VIs-based EOS was caused in part by the effect of changes in soil background on VIs and VIs-based EOS. Our results highlight the need to re-evaluate current LSP data products derived from reflectance-based VIs and to develop new vegetation phenology data products using emitted energy such as SIF.

Assimilating remote sensing-based VPM GPP into the WOFOST model for improving regional winter wheat yield estimation

Crop growth models are powerful tools for predicting crop growth and yield. Gross primary production (GPP) is a major photosynthetic flux that is directly linked to crop grain yield. To better understand the potential of GPP for regional crop yield estimation, in this study, a novel crop data-model assimilation (CDMA) framework was proposed that assimilates accumulative GPP estimates from the satellite-based vegetation photosynthesis model (VPM) into the WOrld FOod STudies (WOFOST) model using the ensemble Kalman filter (EnKF) algorithm to estimate winter wheat GPP and grain yield. Results showed that the WOFOST simulated GPP agreed with the GPPEC derived from eddy flux tower (R2 = 0.74 and 0.47 in 2015 and 2016, respectively). Assimilating GPPVPM into the WOFOST model improved site-scale GPP estimation (R2 = 0.87 and 0.67 in 2015 and 2016, respectively), and also improved regional-scale winter wheat yield estimates (R2 = 0.36 and 0.29; RMSE= 479 and 572 kg/ha in 2015 and 2016, respectively) compared with the open loop simulations (R2 = 0.14 and 0.10; RMSE= 801 and 788 kg/ha in 2015 and 2016, respectively). Our study demonstrated that assimilation of remotely sensed GPP optimized the results of carbon simulation in the WOFOST model and highlighted the potential of GPP for regional winter wheat yield estimation using a data assimilation framework.

Monitoring the impacts of cultivated land quality on crop production capacity in arid regions

Cultivated land plays a vital role in human survival and the development of human civilization. The cultivated land quality (CLQ) is of considerable significance for ensuring food security and social stability. Although studies have been conducted to assess CLQ, efforts regarding the quantitative impacts of CLQ on crop production capacity are still scarce, especially in arid regions. Understanding such a question greatly benefits land resources management and sustainable development of oasis agriculture. The objective of this study is to explore the spatiotemporal variations of CLQ and to quantify the impacts of CLQ on crop production capacity via an ordinary linear regression (OLS) model and correction analysis in arid regions. The Delphi-AHP (analytic hierarchy process) method is applied to assess CLQ in this study with the highest accuracy (96.35%). The average score of cultivated land quality (S-CLQ) is 58.75 in the study area. The results further suggest that the quality of decreasing cultivated land (61.53) is much higher than that of increasing cultivated land (57.99). We use net primary productivity (NPP) calculated by the vegetation photosynthesis model (VPM) to simulate the crop production capacity and find that CLQ has a significant positive impact on crop production capacity (R2 = 0.706). The distribution of average annual NPP is similar to that of CLQ. 62.41% of cultivated land shows an increasing trend in annual NPP, whereas 37.57% of cultivated land shows a decreasing trend. Pearson coefficient by intervals is calculated to identify areas where crop production capacity does not match the quality of cultivated land. The results reveal considerable differences in the Pearson coefficient in various intervals (negative correlation in 5 intervals and positive correlation in 10 intervals). This research quantifies the impacts of CLQ on crop production capacity and provides a way to accurately capture the location of cultivated land that needs to be improved in arid regions.

Annual characteristics of gross primary productivity (GPP) in mangrove forest during 2016-2020 as revealed by Sentinel-2 remote sensing imagery

Carbon dioxide (CO2) is the most responsible gas in the phenomenon of global warming on earth because of its greatest concentration and longevity in the long atmosphere. Meanwhile mangrove is one of the blue carbon parameters that can take CO2 for photosynthesis and store it into biomass and sediment, so the existence of Mangrove plays a key role in the balance of the global carboncycle. Gross Primary Productivity (GPP) is one of the key variables in conducting a carboncycle study because the GPP values constitute the total value of carbon fixation by terrestrial ecosystem through vegetation photosynthesis. The aim of this study is to recognize the annual characteristics of GPP values in the mangrove ecosystem using remote sensing satellite Sentinel-2 during the period of 2016 to 2020 in TAHURA Ngurah Rai, Bali, Indonesia. The vegetation photosynthesis model (VPM) model is used to calculate annual GPP by using remote sensing indices of Enhanced Vegetation Index (EVI) and Land Surface Water Index (LSWI). Both remote sensing indices are supported by temperature and solar radiation data to determine photosynthesis fraction. The results of the current study indicated that the annual GPP values in the mangrove forest of TAHURA Ngurah Rai have decreased during the observation year. The total value of GPP in 2016 is reached to 28790 tC m−2 year−1, while the total amount of GPP value in 2020 decrease to 26223 tC m−2 year−1. The decline in GPP values may be due to changes of land cover and mangrove mortality that occurred around Benoa port in 2018.

Where Anthropogenic Activity Occurs, Anthropogenic Activity Dominates Vegetation Net Primary Productivity Change

Anthropogenic activities and climate change affect the type, structure and function of ecosystems, resulting in important changes in vegetation net primary productivity (NPP). Therefore, in this study we used the vegetation photosynthesis model (VPM) to reveal the spatiotemporal variations in NPP in Xinjiang from 2000 to 2019. The impacts of climate change and anthropogenic activities on NPP changes were quantified and separated by the residual analysis-control variables (RES-CON) method. The results showed that the average NPP in Xinjiang increased by 17.77% from 2000 to 2019. Anthropogenic activities and climate change generally had a positive impact on NPP from 2000 to 2019. The most important anthropogenic activity was land use and land cover (LULC) transformation from grass to arable land, which significantly increased vegetation productivity. Regarding climate change, precipitation has played a significant role in promoting the productivity of vegetation. Overall, the average contribution of climate change (temperature and precipitation) to NPP variation (21.44%) is much greater than the contribution of anthropogenic activities (3.46%), but in areas where anthropogenic activities occur, the average contribution of anthropogenic activities to NPP variation (75.01%) is much greater than the average contribution of climate change (15.53%). Where there are no anthropogenic activities, the average contribution of climate change to NPP variation is 21.72%. In summary, anthropogenic activities are the main driver of NPP variation in areas where anthropogenic activities occur, while the total area in Xinjiang where climate change is the most important driver is larger than the total area where anthropogenic activities are the dominant driver.

Primary productivity estimation of forest based on in-situ biophysical parameters and sentinel satellite data using vegetation photosynthesis model in an eastern Indian tropical dry deciduous forest

Primary productivity estimation of forest based on in-situ biophysical parameters and sentinel satellite data using vegetation photosynthesis model in an eastern Indian tropical dry deciduous forest

Unequal weakening of urbanization and soil salinization on vegetation production capacity

Net Primary Productivity (NPP) has been widely used to estimate the productivity of the farmland ecosystem (FES) and the carbon budget of the terrestrial ecosystem. Changes in the NPP of FES have knock-on effects on food security, sustainable agricultural development, carbon sequestration, and environmental changes. Existing studies mainly focused on the impact of climate change and urbanization on the spatial-temporal pattern of NPP, largely ignoring the roles of soil condition and agricultural management practices (AMPs). At present, the joint impact of “climate-urbanization-soil-AMPs” on the NPP of FES remains unknown. To fill this knowledge gap, we use the NPP dataset retrieved by the vegetation photosynthesis model (VPM), daily meteorological records, field-collected soil data, AMPs data collected from the Government Statistical Yearbook, and land cover dataset to study the joint mechanism of “climate-urbanization-soil” that drives spatial-temporal variations of NPP. Taking the northern slope of Tianshan Mountains as the study area, our results indicate that NPP had increased by 3.86 Tg C from 2000 to 2015 (11.93 Tg C in 2000 and 15.79 Tg C in 2015). From the proposed conceptual framework, we found that climate variables (accumulated temperature, precipitation, evapotranspiration) play a major role in driving the growth of NPP (+4.01 Tg C). The AMPs (i.e., mulching filming, drip irrigation, and fertilizer application) make a positive contribution to NPP increase (+0.98 Tg C). We also found that soil salinization (-1.07 Tg C) weakens the growth of NPP more significantly than urbanization (−0.16 Tg C). This study provides new insights on the mechanism of climate change, urbanization, and soil conditions on crops, benefiting stakeholders in designing better management plans for sustainable agriculture, ecosystem cycling, and food security.

Thermally derived evapotranspiration from the Surface Temperature Initiated Closure (STIC) model improves cropland GPP estimates under dry conditions

Satellite-based gross primary productivity (GPP) monitoring in croplands is challenging due to our limited ability to empirically constrain photosynthetic capacity and associated parameters. Here, we investigated if integrating land surface temperature (TR)-based evapotranspiration (ET) or latent heat flux (λE) into a Remote sensing-driven approach to Coupling Ecosystem Evapotranspiration and Photosynthesis (RCEEP) model, which is modified from the underlying water use efficiency method, can better characterize GPP under dry conditions as compared to the light use efficiency (LUE)-based models. We developed the new GPP model, termed STIC-RCEEP, by combining an ET model, called STIC (Surface Temperature Initiated Closure), and RCEEP. We compared the performance of STIC-RCEEP against four conventional LUE-based models (Vegetation Photosynthesis Model, MOD17, STIC-MOD17, STIC-LUE), using tower-based daily GPP data from different cropland flux sites across the globe. An evaluation of the five GPP models, all optimized using available 170 site-years data from the 22 flux sites, revealed the relatively better performance of the STIC-RCEEP (R2 = 0.78 and RMSE = 2.5 gC m−2 d−1) with respect to the other four LUE models. Error analysis revealed substantially less error of STIC-RCEEP particularly under the dry conditions and RMSE of STIC-RCEEP was 9%–14% less than the other models. This finding highlights the enhanced ability of thermal sensors to capture the water stress signals in ET and GPP, which is also evidenced by the substantially better performance of STIC than another widely-regarded non-thermal ET model (the Priestly Taylor - Jet Propulsion Laboratory model), under dry conditions. The improved GPP estimates from STIC-RCEEP under water-stressed environment opens avenues for further research and applications using existing and new TR-based sensors in coupled crop water use-productivity modeling.

A Modified Vegetation Photosynthesis and Respiration Model (VPRM) for the Eastern USA and Canada, Evaluated With Comparison to Atmospheric Observations and Other Biospheric Models

AbstractAtmospheric CO2 measurements from a dense surface network can help to evaluate terrestrial biosphere model (TBM) simulations of Net Ecosystem Exchange (NEE) with two key benefits. First, gridded CO2 flux estimates can be evaluated over regional scales, not possible using flux tower observations at discrete locations for model evaluation. Second, TBM ability to explain atmospheric CO2 fluctuations due to the biosphere can be directly tested, an important objective for anthropogenic emissions monitoring using atmospheric observations. Here, we customize the Vegetation Photosynthesis and Respiration Model (VPRM) for an eastern North American domain with strong biological activity upwind of urban areas. Parameters are optimized using flux tower observations from a historical database with sites in (and near) the domain. In addition, the respiration model (originally a linear function of temperature) is modified to account for impacts of changing foliage, non‐linear temperature, and water stress. Flux estimates from VPRM, the Carnegie‐Ames‐Stanford Approach (CASA) model and the Simple Biosphere Model v4 (SiB4), are convolved with footprints from atmospheric transport models for evaluation with CO2 observations at 21 towers in the domain, with roughly half of the towers used here for the first time. Results show that the new respiration model in VPRM helps to correct a growing season sink bias in the atmosphere associated with underestimated summertime respiration using the original model with annual parameters. The new VPRM also better explains fine‐scale atmospheric CO2 variability compared to other TBMs, due to higher resolution diagnostic phenology, the new respiration model, domain‐specific parameters, and high‐quality input data sets.