Moderate Resolution Imaging Spectroradiometer Gross Primary Production Research Articles

Importance of the legacy effect for assessing spatiotemporal correspondence between interannual tree-ring width and remote sensing products in the Sierra Nevada

Carbon uptake and tree growth are important factors for assessing productivity and long-term carbon storage. Measurements of radial stem growth are mainly performed at the individual tree scale and can be used to infer ecosystem net primary productivity (NPP). However, these measurements are spatially limited, and remote sensing provides a promising tool to track vegetation function and productivity across spatial scales, making it a viable technique for assessing variation in interannual tree growth and carbon storage. In this study we examined the correspondence between in-situ annual tree-ring width across four dominant evergreen species in the Sierra Nevada and a wide range of remotely sensed products linked to carbon uptake including NPP, gross primary productivity (GPP), net photosynthesis (PSN), normalized difference vegetation index (NDVI), near infrared reflectance of vegetation index (NIRV), and chlorophyll/carotenoid index (CCI) from MODIS (Moderate resolution Imaging Spectroradiometer) as well as downscaled solar-induced fluorescence (SIF) products, using a 14 year dataset (2000–2014) across 62 forest sites. We show that variation of annual tree-ring width was best captured by the annual sum of MODIS GPP, with a legacy effect (5-month backwards shift). Across all forest sites, MODIS GPP with a 5-month legacy effect showed moderate correspondence with tree-ring width (r = 0.60). Within each individual site, however, the interannual correspondence between MODIS GPP with a legacy effect and tree growth was stronger (median r = 0.70 vs 0.14 without a legacy effect). The importance of legacy effects in explaining tree growth variation within sites indicates that tree growth each year is influenced by carbon uptake during the latter part of the previous growing season. Additional local environmental factors also explained annual variation in tree-ring width, including (in descending order of importance) local tree density, latitude, slope, DBH, elevation and aspect.

Spatiotemporal Patterns of Ecosystem Restoration Activities and Their Effects on Changes in Terrestrial Gross Primary Production in Southwest China

Large-scale ecosystem restoration projects (ERPs) have been implemented since the beginning of the new millennium to restore vegetation and improve the ecosystem in Southwest China. However, quantifying the effects of specific restoration activities, such as afforestation and grass planting, on vegetation recovery is difficult due to their incommensurable spatiotemporal distribution. Long-term and successive ERP-driven land use/cover changes (LUCCs) were used to recognise the spatiotemporal patterns of major restoration activities, and a contribution index was defined to assess the effects of these activities on gross primary production (GPP) dynamics in Southwest China during the period of 2001–2015. The results were as follows. (1) Afforestation and grass planting were major restoration activities that accounted for more than 54% of all LUCCs in Southwest China. Approximately 96% of restoration activities involved afforestation, and these activities were mostly distributed around Yunnan Province. (2) The Breathing Earth System Simulator (BESS) GPP performed better than the Moderate Resolution Imaging Spectroradiometer (MODIS) GPP validated by field observation data. Nevertheless, their annual GPP trends were similar and increased by 12,581 g C m−2 d−1 and 13,406 g C m−2 d−1 for MODIS and BESS GPPs, respectively. (3) Although the afforestation and grass planting areas accounted for less than 1% of the total area of Southwest China, they contributed to more than 1% of the annual GPP increase in the entire study area. Afforestation directly contributed 14.94% (BESS GPP) or 24.64% (MODIS GPP) to the annual GPP increase. Meanwhile, grass planting directly contributed only 0.41% (BESS GPP) or 0.03% (MODIS GPP) to the annual GPP increase.

Feasibility of Using MODIS Products to Simulate Sun-Induced Chlorophyll Fluorescence (SIF) in Boreal Forests

Solar-induced chlorophyll fluorescence (SIF) is a novel approach to gain information about plant activity from remote sensing observations. However, there are currently no continuous SIF data produced at high spatial resolutions. Many previous studies have discussed the relationship between SIF and gross primary production (GPP) and showed a significant correlation between them, but few researchers have focused on forests, which are one the most important terrestrial ecosystems. This study takes Greater Khingan Mountains, a typical boreal forest in China, as an example to explore the feasibility of using MODerate resolution Imaging Spectroradiometer (MODIS) products and Orbiting Carbon Observatory-2 (OCO-2) SIF data to simulate continuous SIF at higher spatial resolutions. The results show that there is no significant correlation between SIF and MODIS GPP at a spatial resolution of 1 km; however, significant correlations between SIF and the enhanced vegetation index (EVI) were found during growing seasons. Furthermore, the broadleaf forest has a higher SIF than coniferous forest because of the difference in leaf and canopy bio-chemical and structural characteristic. When using MODIS EVI to model SIF, linear regression models show average performance (R2 = 0.58, Root Mean Squared Error (RMSE) = 0.14 from Julian day 145 to 257) at a 16-day time scale. However, when using MODIS EVI and temperature, multiple regressions perform better (R2 = 0.71, RMSE = 0.13 from Julian day 145 to 241). An important contribution of this paper is the analysis of the relationships between SIF and vegetation indices at different spatial resolutions and the finding that the relationships became closer with a decrease in spatial resolution. From this research, we conclude that the SIF of the boreal forest investigated can mainly be explained by EVI and air temperature.

A Comparison of OCO-2 SIF, MODIS GPP, and GOSIF Data from Gross Primary Production (GPP) Estimation and Seasonal Cycles in North America

Remotely sensed products are of great significance to estimating global gross primary production (GPP), which helps to provide insight into climate change and the carbon cycle. Nowadays, there are three types of emerging remotely sensed products that can be used to estimate GPP, namely, MODIS GPP (Moderate Resolution Imaging Spectroradiometer GPP, MYD17A2H), OCO-2 SIF, and GOSIF. In this study, we evaluated the performances of three products for estimating GPP and compared with GPP of eddy covariance(EC) from the perspectives of a single tower (23 flux towers) and vegetation types (evergreen needleleaf forests, deciduous broadleaf forests, open shrublands, grasslands, closed shrublands, mixed forests, permeland wetlands, and croplands) in North America. The results revealed that sun-induced chlorophyll fluorescence (SIF) data and MODIS GPP data were highly correlated with the GPP of flux towers (GPPEC). GOSIF and OCO-2 SIF products exhibit a higher accuracy in GPP estimation at the a single tower (GOSIF: R2 = 0.13–0.88, p < 0.001; OCO-2 SIF: R2 = 0.11–0.99, p < 0.001; MODIS GPP: R2 = 0.15–0.79, p < 0.001). MODIS GPP demonstrates a high correlation with GPPEC in terms of the vegetation type, but it underestimates the GPP by 1.157 to 3.884 gCm−2day−1 for eight vegetation types. The seasonal cycles of GOSIF and MODIS GPP are consistent with that of GPPEC for most vegetation types, in spite of an evident advanced seasonal cycle for grasslands and evergreen needleleaf forests. Moreover, the results show that the observation mode of OCO-2 has an evident impact on the accuracy of estimating GPP using OCO-2 SIF products. In general, compared with the other two datasets, the GOSIF dataset exhibits the best performance in estimating GPP, regardless of the extraction range. The long time period of MODIS GPP products can help in the monitoring of the growth trend of vegetation and the change trends of GPP.

Contrasting Performance of the Remotely-Derived GPP Products over Different Climate Zones across China

Precise quantification of terrestrial gross primary production (GPP) has been recognized as one of the most important components in understanding the carbon balance between the biosphere and the atmosphere. In recent years, although many large-scale GPP estimates from satellite data and ecosystem models have been generated, few attempts have been made to compare the different GPP products at national scales, particularly for various climate zones. In this study, two of the most widely-used GPP datasets were systematically compared over the eight climate zones across China’s terrestrial ecosystems from 2001 to 2015, which included the moderate resolution imaging spectroradiometer (MODIS) GPP and the breathing Earth system simulator (BESS) GPP products. Additionally, the coarse (0.05o) GPP estimates from the vegetation photosynthesis model (VPM) at the same time scale were used for auxiliary analysis with the two products. Both MODIS and BESS products exhibited a decreasing trend from the southeast region to the northwest inland. The largest GPP was found in the tropical humid region with 5.49 g C m−2 d−1 and 5.07 g C m−2 d−1 for MODIS and BESS, respectively, while the lowest GPP was distributed in the warm temperate arid region, midtemperate semiarid region and plateau zone. Meanwhile, the work confirmed that all these GPP products showed apparent seasonality with the peaks in the summertime. However, large differences were found in the interannual variations across the three GPP products over different climate regions. Generally, the BESS GPP agreed better than the MODIS GPP when compared to the seasonal and interannual variations of VPM GPP. Furthermore, the spatial correlation analysis between terrestrial GPP and the climatic factors, including temperature and precipitation, indicated that natural rainfall dominated the variability in GPP of Northern China, such as the midtemperate semiarid region, while temperature was a key controlling factor in the Southern China and the Tibet Plateau area.

Contrasting the Performance of Eight Satellite-Based GPP Models in Water-Limited and Temperature-Limited Grassland Ecosystems

Models constitute the primary approaches for predicting terrestrial ecosystem gross primary production (GPP) at regional and global scales. Many satellite-based GPP models have been developed due to the simple algorithms and the low requirements of model inputs. The performances of these models are well documented at the biome level. However, their performances among vegetation subtypes limited by different environmental stresses within a biome remains largely unexplored. Taking grasslands in northern China as an example, we compared the performance of eight satellite-based GPP models, including three light-use efficiency (LUE) models (vegetation photosynthesis model (VPM), modified VPM (MVPM), and moderate resolution imaging spectroradiometer GPP algorithm (MODIS-GPP)) and five statistical models (temperature and greenness model (TG), greenness and radiation model (GR), vegetation index model (VI), alpine vegetation model (AVM), and photosynthetic capacity model (PCM)), between the water-limited temperate steppe and the temperature-limited alpine meadow based on 16 site-year GPP estimates at four eddy covariance (EC) flux towers. The results showed that all the GPP models performed better in the alpine meadow, particularly in the alpine shrub meadow (R2 ≥ 0.84), than in the temperate steppe (R2 ≤ 0.68). The performance varied greatly among the models in the temperate steppe, while slight intermodel differences existed in the alpine meadow. Overall, MVPM (of the LUE models) and VI (of the statistical models) were the two best-performing models in the temperate steppe due to their better representation of the effect of water stress on vegetation productivity. Additionally, we found that the relatively worse model performances in the temperate steppe were seriously exaggerated by drought events, which may occur more frequently in the future. This study highlights the varying performances of satellite-based GPP models among vegetation subtypes of a biome in different precipitation years and suggests priorities for improving the water stress variables of these models in future efforts.

Optimizing a remote sensing production efficiency model for macro-scale GPP and yield estimation in agroecosystems

Earth observation data are increasingly used to provide consistent eco-physiological information over large areas through time. Production efficiency models (PEMs) estimate Gross Primary Production (GPP) as a function of the fraction of photosynthetically active radiation absorbed by the canopy, which is derived from Earth observation. GPP can be summed over the growing season and adjusted by a crop-specific harvest index to estimate yield. Although PEMs have many advantages over other crop yield models, they are not widely used, because performance is relatively poor. Here, a new PEM is presented that addresses deficiencies for macro-scale application: Production Efficiency Model Optimized for Crops (PEMOC). It was developed by optimizing functions from the literature with GPP estimated by eddy covariance flux towers in the United States. The model was evaluated using newly developed Earth observation products and county-level yield statistics for major crops. PEMOC generally performed better at the field and county level than another commonly used PEM, the Moderate Resolution Imaging Spectroradiometer GPP (MOD17). PEMOC and MOD17 estimates of GPP had an R2 and root mean squared error (RMSE) over the growing season of 0.71–0.89 (9.87–17.47 g CO2 d−1) and 0.59–0.83 (6.86–22.20 g CO2 d−1) with flux tower GPP. PEMOC produced R2s and RMSE of 0.70 (0.52), 0.60 (0.61), and 0.62 (0.59), while MOD17 produced R2s and RMSE of 0.65 (0.57), 0.53 (0.66), and 0.65 (0.57) with corn, soybean, and winter wheat crop yield anomalies. The sample size of rice was small, so yields were compared directly. PEMOC and MOD17 produced R2s and RMSE of 0.53 (3.42 t ha−1) and 0.40 (4.89 t ha−1). The most sizeable model improvements were seen for C3 and C4 crops during emergence/senescence and peak season, respectively. These improvements were attributed to C3 and C4 partitioning, optimized temperature and moisture constraints, and an evapotranspiration-based soil moisture index.

Assessment of the water cycle impact by the Budyko curve on watershed hydrology using SWAT and CO2 concentrations derived from Terra MODIS GPP

Greenhouse gas and aerosol emissions from human activities continue to alter the climate and are likely to have significant impacts on watershed hydrological cycles and stream water quality. We applied the Soil and Water Assessment Tool (SWAT) to evaluate how CO2 impacts the hydrology of a watershed. The goal of this study is to evaluate how potential CO2 changes would impact the hydrologic components in the forest-dominant Seolma-cheon watershed (8.48 km2) in South Korea. Using an image from the Moderate Resolution Imaging Spectroradiometer (MODIS) onboard Terra, the CO2 flux was estimated as the sum of the Gross Primary Productivity (GPP) and Ecosystem Respiration (Rec) following the method of Lloyd and Taylor (1994). To assess the water cycle impact, the SWAT results for certain hydrological components, such as total runoff (TQ), surface runoff (SQ), lateral runoff (LQ), soil water content (SW) and evapotranspiration (ET), were analysed to generate Budyko curves. In the field of watershed management, SWAT modelling using CO2 concentrations as an additional parameter provides a better understanding of the role of ET and soil moisture in ongoing and future climate changes.

Spatial Downscaling of Gross Primary Productivity Using Topographic and Vegetation Heterogeneity Information: A Case Study in the Gongga Mountain Region of China

Due to the spatial heterogeneity of land surfaces, downscaling is an important issue in the development of carbon cycle models when evaluating the role of ecosystems in the global carbon cycle. In this study, a downscaling algorithm was developed to model gross primary productivity (GPP) at 500 m in a time series over rugged terrain, which considered the effects of spatial heterogeneity on carbon flux simulations. This work was carried out for a mountainous area with an altitude ranging from 2606 to 4744 m over the Gongga Mountain (Sichuan Province, China). In addition, the Moderate Resolution Imaging Spectroradiometer (MODIS) GPP product at 1 km served as the primary dataset for the downscaling algorithm, and the 500 m MODIS GPP product was used as the reference dataset to evaluate the downscaled GPP results. Moreover, in order to illustrate the advantages and benefits of the proposed downscaling method, the downscaled results in this work, along with ordinary kriging downscaled results, spline downscaled results and inverse distance weighted (IDW) downscaled results, were compared to the MODIS GPP at 500 m. The results showed that (1) the GPP difference between the 500 m MODIS GPP and the proposed downscaled GPP results was primarily in the range of [−1, 1], showing that both vegetation heterogeneity factors (i.e., LAI) and topographic factors (i.e., altitude, slope and aspect) were useful for GPP downscaling; (2) the proposed downscaled results (R2 = 0.89, RMSE = 1.03) had a stronger consistency with the 500 m MODIS GPP than those of the ordinary kriging downscaled results (R2 = 0.43, RMSE = 1.36), the spline downscaled results (R2 = 0.40, RMSE = 1.50) and the IDW downscaled results (R2 = 0.42, RMSE = 1.10) for all Julian days; and (3) the inconsistency between MODIS GPP at 500 m and 1 km increased with the increase in altitude and slope. The proposed downscaling algorithm could provide a reference when considering the effects of spatial heterogeneity on carbon flux simulations and retrieving other fine resolution ecological-physiology parameters (e.g., net primary productivity and evaporation) over topographically complex terrains.

Estimation of Global Vegetation Productivity from Global LAnd Surface Satellite Data

Accurately estimating vegetation productivity is important in research on terrestrial ecosystems, carbon cycles and climate change. Eight-day gross primary production (GPP) and annual net primary production (NPP) are contained in MODerate Resolution Imaging Spectroradiometer (MODIS) products (MOD17), which are considered the first operational datasets for monitoring global vegetation productivity. However, the cloud-contaminated MODIS leaf area index (LAI) and Fraction of Photosynthetically Active Radiation (FPAR) retrievals may introduce some considerable errors to MODIS GPP and NPP products. In this paper, global eight-day GPP and eight-day NPP were first estimated based on Global LAnd Surface Satellite (GLASS) LAI and FPAR products. Then, GPP and NPP estimates were validated by FLUXNET GPP data and BigFoot NPP data and were compared with MODIS GPP and NPP products. Compared with MODIS GPP, a time series showed that estimated GLASS GPP in our study was more temporally continuous and spatially complete with smoother trajectories. Validated with FLUXNET GPP and BigFoot NPP, we demonstrated that estimated GLASS GPP and NPP achieved higher precision for most vegetation types.

Interplay of drought and tropical cyclone activity in SE U.S. gross primary productivity

AbstractTropical cyclones (TCs), often associated with massive flooding and landslides in the Southeast U.S. (SE U.S.), provide a significant input of freshwater to the hydrologic system, and their timing and trajectory significantly impact drought severity and persistence. This manuscript investigates the sensitivity of gross primary productivity (GPP) in the SE U.S. to TC activity using the 1‐D column implementation of the Duke Coupled Hydrology Model with Vegetation (DCHM‐V) including coupled water and energy cycles and a biochemical representation of photosynthesis. Decadal‐scale simulations of water, energy, and carbon fluxes were conducted at high temporal (30 min) and spatial (4 km) resolution over the period 2002–2012. At local scales, model results without calibration compare well against AmeriFlux tower data. At regional scales, differences between the DCHM‐V estimates and the Moderate Resolution Imaging Spectroradiometer GPP product reflect the spatial organization of soil hydraulic properties and soil moisture dynamics by physiographic region, highlighting the links between the water and carbon cycles. To isolate the contribution of TC precipitation to SE U.S. productivity, control forcing simulations are contrasted with simulations where periods of TC activity in the atmospheric forcing data were replaced with climatology. During wet years, TC activity impacts productivity in 40–50% of the SE U.S. domain and explains a regional GPP increase of 3–5 Mg C/m2 that is 9% of the warm season total. In dry years, 23–34% of the domain exhibits a smaller positive response that corresponds to 4–8% of the seasonal carbon uptake, depending on TC timing and trajectory.

Development of a coupled carbon and water model for estimating global gross primary productivity and evapotranspiration based on eddy flux and remote sensing data

Terrestrial gross primary productivity (GPP) and evapotranspiration (ET) are two key ecosystem fluxes in the global carbon and water cycles. As carbon and water fluxes are inherently linked, knowing one provides information for the other. However, tightly coupled and easy to use ecosystem models are rare and there are still large uncertainties in global carbon and water flux estimates. In this study, we developed a new monthly coupled carbon and water (CCW) model. GPP was estimated based on the light-use efficiency (LUE) theory that considered the effect of diffuse radiation, while ET was modeled based on GPP and water-use efficiency (WUE). We evaluated the non-linear effect of single (GPPOR) or combined (GPPAND) limitations of temperature and vapor pressure deficit on GPP. We further compared the effects of three types of WUE (i.e., WUE, inherent WUE, and underlying WUE) on ET (i.e., ETWUE, ETIWUE and ETUWUE). CCW was calibrated and validated using global eddy covariance measurement from FLUXNET and remote sensing data from Moderate Resolution Imaging Spectroradiometer (MODIS) from 2000 to 2007. Modeled GPPAND and GPPOR explained 67.3% and 66.8% of variations of tower-derived GPP, respectively, while ETUWUE, ETIWUE and ETWUE explained 65.7%, 59.9% and 58.1% of tower-measured ET, respectively. Consequently, we chose GPPAND and ETUWUE as the best modeling framework for CCW, and estimated global GPP as 134.2PgCyr−1 and ET as 57.0×103km3 for vegetated areas in 2001. Global ET estimated by CCW compared favorably with MODIS ET (60.5×103km3) and ET derived from global precipitation (56.5×103km3). However, global GPP estimated by CCW was about 19% higher than MODIS GPP (109.0PgCyr−1). The mean global WUE value estimated by CCW (2.35gCkg−1H2O) was close to the mean tower-based WUE (2.60gCkg−1H2O), but was much higher than the WUE derived from MODIS products (1.80gCkg−1H2O). We concluded that the new simple CCW model provided improved estimates of GPP and ET. The biome-specific parameters derived in this study allow CCW to be further linked with land use change models to project human impacts on terrestrial ecosystem functions.

Modeling of maize gross primary production using MODIS imagery and flux tower data.

Maize is one of the most important crops cultivated on the global scale. Accurate estimation of maize Gross Primary Production (GPP) can provide valuable information for regional and global carbon budget studies. From site level to regional/global scales, GPP estimation depends on remote sensing or eddy covariance flux data. In this research, the 8-day composite GPP of maize was estimated by Moderate Resolution Imaging Spectroradiometer (MODIS) and flux tower data at eight study sites using a Regional Production Efficiency Model (REG-PEM). The performance of the model was assessed by analyzing the linearly regression of GPP estimated from the REG-PEM model (GPPEST) with the GPP predicted from the eddy covariance data (GPPEC). The coefficient of determination, root mean squared error and mean absolute error of the regression model were calculated. The uncertainties of the model are also discussed in this research. The seasonal dynamics (phases and magnitudes) of the GPPEST reasonably agreed with those of GPPEC, indicating the potential of the satellite-driven REG-PEM model for up-scaling the GPP in maize croplands. Furthermore, the maize GPP estimated by this model is more accurate than the MODIS GPP products (MOD17A2). In particular, MOD17A2 significantly underestimated the GPP of maize croplands. The uncertainties in the REG-PEM model are mostly contributed by the maximum light use efficiency and the fraction of photosynthetically active radiation. Keywords: gross primary production, maize, light use efficiency, MODIS, remote sensing, flux data DOI: 10.3965/j.ijabe.20160902.2245 Citation: Li S H, He P, Liu B S, Ni P, Han X. Modeling of maize gross primary production using MODIS imagery and flux tower data. Int J Agric & Biol Eng, 2016; 9(2): 110－118.

Modeling Surface Climate in U.S. Cities Using Simple Biosphere Model SiB2

We combine Landsat-and the moderate-resolution imaging spectroradiometer (MODIS)-based products in the Simple Biosphere model (SiB2) to assess the effects of urbanized land on the continental United States (CONUS) surface climate. Using National Land Cover Database (NLCD) impervious surface area (ISA), we define more than 300 urban settlements and their surrounding suburban and rural areas over the CONUS. The SiB2 modeled gross primary production (GPP) over the CONUS of 7.12 PgC (1 PgC = 1015 g of carbon) is comparable to the MODIS improved GPP of 6.29 PgC. At state level, SiB2 GPP is highly correlated with MODIS GPP, with a correlation coefficient of 0.94. An increasing horizontal GPP gradient is shown from the urban out to the rural area, with, on average, rural areas fixing 30% more GPP than urbans. Cities built in forested biomes have stronger urban heat island (UHI) magnitude than those built in short vegetation with low biomass. Mediterranean climate cities have a stronger UHI in wet season than in dry season. Our results also show that for urban areas built within forests, 39% of the precipitation is discharged as surface runoff during summer versus 23% in rural areas.

Tracking Ecosystem Water Use Efficiency of Cropland by Exclusive Use of MODIS EVI Data

One of the most important linkages that couple terrestrial carbon and water cycles is ecosystem water use efficiency (WUE), which is relevant to the reasonable utilization of water resources and farming practices. Eddy covariance techniques provide an opportunity to monitor the variability in WUE and can be integrated with Moderate Resolution Imaging Spectroradiometer (MODIS) observations. Scaling up in situ observations from flux tower sites to large areas remains challenging and few studies have been reported on direct estimation of WUE from remotely-sensed data. This study examined the main environmental factors driving the variability in WUE of corn/soybean croplands, and revealed the prominent role of solar radiation and temperature. Time-series of MODIS-derived enhanced vegetation indices (EVI), which are proxies for the plant responses to environmental controls, were also strongly correlated with ecosystem WUE, thereby implying great potential for remote quantification. Further, both performance of the indirect MODIS-derived WUE from gross primary productivity (GPP) and evapotranspiration (ET), and the direct estimates by exclusive use of MODIS EVI data were evaluated using tower-based measurements. The results showed that ecosystem WUE were overpredicted at the beginning and ending of crop-growth periods and severely underestimated during the peak periods by the indirect estimates from MODIS products, which was mainly attributed to the error source from MODIS GPP. However, a simple empirical model that is solely based on MODIS EVI data performed rather well to capture the seasonal variations in WUE, especially for the growing periods of croplands. Independent validation at different sites indicates the method has potential for broad application.

Improving the quantification of terrestrial ecosystem carbon dynamics over the United States using an adjoint method

We used an adjoint version of the process‐based Terrestrial Ecosystem Model (TEM) to optimize the model parameters in a spatially explicit manner by assimilating satellite‐based estimates of gross primary production (GPP) in the conterminous United States. Traditionally, terrestrial ecosystem model parameterization is conducted at site‐level for various plant functional types (PFTs). The optimal parameters are then extrapolated to regions that have the same plant functional types as parameterized. However, site‐level parameterization might be only valid within a few kilometers in the footprint of the individual site. Extrapolation of the optimal parameters to a region with an assumption that the parameters are the same for all pixels for the same ecosystem type in the region may introduce significant errors in quantification of regional carbon dynamics. This study used Moderate Resolution Imaging Spectroradiometer GPP to optimize parameters for each pixel using adjoint method in a spatially explicit manner. The spatially explicit parameters were then used to quantify the regional carbon fluxes from 2000 to 2005. The estimated net ecosystem production (NEP) was used to drive a global atmospheric transport model, GEOS‐Chem, to estimate the near surface atmospheric CO2 concentrations, which were then compared with flask measurements. We found that (1) the site‐level TEM parameterization method provided good estimates of carbon fluxes at site levels, but had a large uncertainty in the regional simulations; (2) the spatially explicit parameterization improved the estimates of the spatial distribution and seasonal variation of regional carbon dynamics; and (3) when driven with the NEP estimated with the spatially explicit model, the GEOS‐Chem captured the seasonal trend of near‐surface CO2 concentrations better than that was driven with the estimates based on the site‐level parameterized model. This study suggested that future quantification of regional carbon dynamics should consider the spatial variation of parameters, which could be optimized using spatially explicit carbon flux data.

Effects of forest type and environmental factors on forest carbon use efficiency assessed using MODIS and FIA data across the eastern USA

The carbon use efficiency (CUE) of a forest, calculated as the ratio of net primary productivity (NPP) to gross primary productivity (GPP), measures how efficiently a forest sequesters atmospheric carbon. Some prior research has suggested that CUE varies with environmental conditions, while other suggests that CUE is constant. Research using Moderate Resolution Imaging Spectroradiometer (MODIS) data has indicated a variable CUE, but those results are suspected because MODIS NPP data have not been well validated.We tested two questions. First, whether MODIS CUE is constant or whether it varies by forest type, climate, and geographic factors across the eastern USA. Second, whether those results occur when field-based NPP data are employed. We used MODIS model-based estimates of GPP and NPP, and forest inventory and anlaysis (FIA) field-based estimates of NPP data. We calculated two estimates of CUE for forest in 390 km2 hexagons: (1) MODIS CUE as MODIS NPP divided by MODIS GPP and (2) F/M ZCUE as the standardized difference between FIA NPP and MODIS GPP.MODIS CUE and F/M ZCUE both varied similarly and significantly in relation to forest type, and climatic and geographic factors, strongly supporting a variable rather than a constant CUE. The CUE was significantly higher in deciduous than in mixed and evergreen forests. Regression models indicated that CUE decreased with increases in temperature and precipitation and increased with latitude and altitude. The similar trends in MODIS CUE and F/M ZCUE support the use of the more easily obtained MODIS CUE.

Evaluation of MODIS gross primary productivity and land cover products for the humid tropics using oil palm trees in Peninsular Malaysia and Google Earth imagery

Conducting quantitative studies on the carbon balance or productivity of oil palm is important in understanding the role of this ecosystem in global climate change. In this study, we evaluated the accuracy of MODIS (Moderate Resolution Imaging Spectroradiometer) annual gross primary productivity (GPP) (the product termed MOD-17) and its upstream products, especially the MODIS land cover product (the product termed MOD-12). We used high-resolution Google Earth images to classify the land cover classes and their percentage cover within each 1 km spatial resolution MODIS pixel. We used field-based annual GPP for 2006 to estimate GPP for each pixel based on percentage cover. Both land cover and GPP were then compared to MODIS land cover and GPP products. The results show that for pure pixels that are 100% covered by mature oil palm trees, the RMSE (root mean square error) between MODIS and field-based annual GPP is 18%, and that this is increased to 27% for pixels containing mostly oil palm. Overall, for an area of about 42 km2 the RMSE is 26%. We conclude that land cover classification (at 1 km resolution) is one of the main factors for the discrepancy between MODIS and field-based GPP. We also conclude that the accuracy of the MODIS GPP product could be improved significantly by using higher-resolution land cover maps.

Spatial-temporal variability of terrestrial vegetation productivity in the Yangtze River Basin during 2000-2009

Aims Terrestrial net primary production (NPP), the balance of gross primary production (GPP) and autotrophic respiration (AR), is a critical measure of carbon sequestration capacity for the Earth’s land surface. The aim of this study was to understand the spatio-temporal variability of NPP associated with GPP and AR in the Yangtze River Basin (YRB), China, from 2000 to 2009 during which the basin warmed significantly. Methods We first derived AR and carbon-use efficiency (CUE) from the improved Moderate Resolution Imaging Spectroradiometer GPP/ NPP products (MOD17) and then conducted spatial analysis to quantify how NPP relates to GPP, AR and their relationship with key observed climate variables (temperature, precipitation and sunshine percentage) in the YRB during 2000–9. Important Findings The spatial pattern of NPP in the YRB was predominantly deter mined by GPP and further modified by AR. Higher GPP and relatively low AR made the southern Jinshajiang sub-basin the most productive area in NPP in the YRB. A large portion of the YRB experienced a warmer and drier climate trend in the growing season during 2000–9. In the upper reaches of the basin, possessing a relatively low temperature base, increases in temperature led to greater increases in GPP than those in AR, resulting in greater increased NPP. However, in the middle and lower reaches of the basin where the base temperature is relatively high, increases in temperature led to greater increases in AR than those in GPP, leading to decreases in NPP. Overall, 86.7% of the vegetated area showed a consistent GPP and NPP trend through time with 71.3% of the vegetated area having a positive trend both in GPP and NPP, and the remaining 13.3% of vegetated areas showed an opposite trend in GPP and NPP, with positive GPP and negative NPP trajectories dominating (10.1% of vegetated area) the trend. Although climate warming generally had positive effects on vegetation growth in most areas of the basin, areas with increased NPP (74.5%) were less extensive than those with increased GPP (81.4%) due to the wider increase in AR (82.2%). During the study period, increases in AR offset 62% of the total increased GPP, leading to a substantial decline of CUE, particularly in the warmer lower altitude regions in the southeast. Our work reveals the diverse responses of NPP associated with GPP and AR as the climate warms and generally suggests that NPP in the middle and lower sub-basins in the YRB is more sensitive to future climate warming. These findings enhance our understanding of terrestrial ecosystem carbon dynamics in response to global warming and provide a scientific basis for managing ecosystem productivity in the YRB, China.

Evaluation and improvement of MODIS gross primary productivity in typical forest ecosystems of East Asia based on eddy covariance measurements

Gross primary productivity (GPP) is a major component of carbon exchange between the atmosphere and terrestrial ecosystems and a key component of the terrestrial carbon cycle. Because of the large spatial heterogeneity and temporal dynamics of ecosystems, it is a challenge to estimate GPP accurately at global or regional scales. The 8-day MODerate resolution Imaging Spectroradiometer (MODIS) GPP product provides a near real time estimate of global GPP. However, previous studies indicated that MODIS GPP has large uncertainties, partly caused by biases in parameterization and forcing data. In this study, MODIS GPP was validated using GPP derived from the eddy covariance flux measurements at five typical forest sites in East Asia. The validation indicated that MODIS GPP was seriously underestimated in these forest ecosystems of East Asia, especially at northern sites. With observed meteorological data, fraction of photosynthetically active radiation absorbed by the plant canopy (fPAR) calculated using smoothed MODIS leaf area index, and optimized maximum light use efficiency (εmax) to force the MOD17 algorithm, the agreement between predicted GPP and tower-based GPP was significantly improved. The errors of MODIS GPP in these forest ecosystems of East Asia were mainly caused by uncertainties in εmax, followed by those in fPAR and meteorological data. The separation of canopy into sunlit and shaded leaves, for which GPP is individually calculated, can improve GPP simulation significantly.