MODIS Gross Primary Production Research Articles

A dynamic-leaf light use efficiency model for improving gross primary production estimation

Accurate quantification of terrestrial gross primary production (GPP) is integral for enhancing our understanding of the global carbon budget and climate change. The light use efficiency (LUE) model is undoubtedly the most extensively applied method for GPP estimation. However, the two-leaf (TL)-LUE model using a ‘potential’ sunlit leaf area index (LAIsu) can separate a portion of LAIsu even when the canopy does not receive any direct radiation, leading to the underestimation of GPP under cloudy and overcast days. Here, we developed a dynamic-leaf (DL) LUE model by introducing an ‘effective’ LAIsu to improve GPP estimation, which considers the comprehensive contribution of LAIsu when the canopy does and does not receive direct radiation. In particular, the new model decreases LAIsu to zero when direct radiation reaches zero. Our evaluation at eight ChinaFLUX sites showed that (1) the DL-LUE model outperformed the most well-known BL-LUE (namely, the MOD17 GPP algorithm) and TL-LUE models in reproducing the daily in situ GPP, especially at four forest sites [reducing the root mean square error (RMSE) from 1.74 g C m−2 d−1 and 1.53 g C m−2 d−1 to 1.36 g C m−2 d−1 and increasing the coefficient of determination (R 2) from 0.74 and 0.79–0.82, respectively]. Moreover, the improvements were particularly pronounced at longer temporal scales, as indicated by the RMSE decreasing from 29.32 g C m−2 month−1 and28.11 g C m−2 month−1 to 25.81 g C m−2 month−1 at a monthly scale and from 231.82 g C m−2 yr−1 and 221.60 g C m−2 yr−1–200.00 g C m−2 yr−1 at a yearly scale; (2) the DL-LUE model mitigated the systematic underestimation of the in situ GPP by both the TL-LUE and BL-LUE models when the clearness index (CI) was below 0.5, as indicated by the Bias reductions of 0.25 g C m−2 d−1 and 0.46 g C m−2 d−1, respectively; and (3) the contributions of the shaded GPP to the total GPP from the DL-LUE model were higher by 0.07–0.16 than those from the TL-LUE model across the eight ChinaFLUX sites. The proposed parsimonious and effective DL-LUE model not only has great potential for improving global GPP estimations but also provides a more mechanism-based approach for partitioning the total GPP into its shaded and sunlit components.

A novel approach for retrieving GPP of evergreen forest regions of India using random forest regression

Gross Primary Productivity (GPP) is a crucial variable of global carbon cycle for determining the ecosystem’s health. Various methods are devised to quantify GPP and upscale it in both time and space. The most common methods are physical model and eddy covariance-based estimation, which are very restricted to surrounding area of study, only. The alternative methods are empirical (e.g., LUE, CASA, SCARF, and MODIS) and Machine learning (ML) models that employ remote sensing satellite data and geographical factors. However, for using ML models, ground-based measurements of GPP is a very important factor, which is not available in most places. We propose an alternative and effective way of estimating the GPP using the ML model and data from various flux sites around the globe for a particular plant functional type (PFT). In the present study, RF is used as ML model, which is trained on global GPP data from evergreen forest and implemented in Indian region. The key findings indicated that ML-based GPP is highly accurate and hence, we generated 20 years of time series GPP dataset (2001–2020). We validated with ground-based flux tower observations during 2016–2018 for three sites (very limited datasets) in India and compared them with MODIS GPP. The coefficient of determination (R2) value of the ML-based model was 0.84 with root mean square error (RMSE) of 1.45 gC m−2 Day−1 and mean absolute error (MAE) of 0.838 gC m−2 Day−1. The proposed approach is highly accurate and far better than the MODIS-based GPP. Therefore, it can be further extended to other forest types for a holistic assessment of the carbon cycle of a region.

Spatial and temporal variations of vegetation water use efficiency and its response to climate change and human activities in the West Liao River Plain, China

Water use efficiency [WUE = gross primary production (GPP)/evapotranspiration (ET)] is an important indicator of the degree of coupling between carbon and water cycles in ecosystems. However, the response of the carbon and water cycles to climate change and human activities,as well as the underlying driving mechanisms in the West Liao River Plain (WLRP), a typical farming–pasturing ecotone in northern China, remain unclear. This study examined the temporal and spatial variation characteristics of WUE in the WLRP from 2000 to 2020 using linear regression and the coefficient of variation (CV) method based on MODIS GPP and ET datasets. The relationships between WUE, meteorological factors, and human activities as well as the mechanism driving WUE changes were revealed through correlation analyses, residual analysis, and the grey correlation model. The interannual change of WUE from 2000 to 2020 showed a fluctuating but weakly upward trend. The intra-annual change in WUE followed an M-type bimodal trend, with two peaks from May to June and August to September. Areas with increased WUE accounted for 50.82% of the study area, and 11.11% of these showed a significant increasing trend. WUE was mainly positively correlated with temperature and solar radiation and negatively correlated with precipitation and VPD and presented obvious regional differences. Solar radiation had the most significant impact on WUE. WUE change is not entirely driven by climate change, and human activities have also played an important role. In areas where WUE increased, The average contribution rate of climate change was 72.4%, and that of human activities was 27.6%. This study reveals the temporal and spatial dynamics of WUE in the WLRP and highlights the influence of human activities on WUE changes.

Using enhanced vegetation index and land surface temperature to reconstruct the solar-induced chlorophyll fluorescence of forests and grasslands across latitude and phenology

IntroductionForest and grassland are the two main carbon-collecting terrestrial ecosystems, and detecting their solar-induced chlorophyll fluorescence (SIF) enables evaluation of their photosynthetic intensity and carbon-collecting capacity. Since SIF that is retrieved directly from satellite observations suffers from low spatial resolution, discontinuity, or low temporal resolution, some vegetation indexes (VIs) and meteorological factors are used as predictors to reconstruct SIF products. Yet, unlike VIs, certain meteorological factors feature a relatively low space resolution and their observations are not always accessible. This study aimed to explore the potential of reconstructing SIF from fewer predictors whose high-resolution observations are easily accessible.MethodsA total of six forest and grassland regions across low, mid, and high latitudes were selected, and the commonly used predictors-normalized difference vegetation index (NDVI), enhanced vegetation index (EVI), and land surface temperature (LST)—were compared for their correlation with SIF. Results show that the combination of EVI and LST is more strongly correlated with SIF, but each contributed differently to SIF at differing growth stages of forest and grassland. Accordingly, we proposed the idea of a combined sampling approach that considers both location and phenological phase, to explore the extent to which time and space coverage samples' span could enlarge the disparity of EVI data in particular regions at specific growth stages. To do that, three kinds of sample combination methods were proposed: monthly regression at a global scale, seasonal regression at a regional scale, and monthly regression at a regional scale. Following this, Sentinel-3 EVI and MODIS LST data were used to reconstruct 500 m SIF in the six regions by implementing the proposed methodology.Results and discussionThese results showed that the R2 values were ≥0.90 between the reconstructed SIF and MODIS GPP (gross primary productivity), 0.70 with GOME-2 SIF and 0.77 with GOSIF, thus proving the proposed methodology could produce reliable results for reconstruction of 500 m SIF. This proposed approach, which bypasses dependence of traditional SIF reconstruction model on numerous predictors not easy to obtain, can serve as a better option for more efficient and accurate high-resolution SIF reconstructions in the future.

An improved light use efficiency model by considering canopy nitrogen concentrations and multiple environmental factors

Although the light use efficiency (LUE) models are widely employed to estimate ecosystem gross primary production (GPP), the majority of these models inadequately consider the effects of environmental and biological factors on GPP, resulting in considerable uncertainty. In addition, most developed LUE models have assumed that the maximum LUE (εmax) is a fixed value for different vegetation types, while εmax should be dynamic under environmental changes. The canopy nitrogen (N) concentrations were considered to have a significant linear relationship with εmax and could be estimated using various vegetation indices. In this study, we selected a vegetation index to characterize the canopy N concentrations and further simulate the dynamic εmax. We then developed an improved LUE model that simultaneously integrated the effects of canopy N concentrations, temperature, water, atmospheric carbon dioxide (CO2) and radiation components on the GPP estimates. Different forms of LUE models that partially integrate the above factors were also constructed for comparison. Our results showed that (1) the green chlorophyll index (CIgreen) correlated well with measured canopy N concentrations (R2 = 0.68), and the model using the CIgreen to characterize canopy N concentrations performed the best; (2) the GPP estimated using the improved model gave the best accuracy (R2 = 0.69, RMSE = 2.13 gC/m2/d, MAE=1.36 gC/m2/d, IOA = 0.915) and performed well for different vegetation types when validated against the FLUXNET GPP; and (3) the estimated GPP had the best accuracy compared with MOD17 GPP and the revised EC-LUE GPP on a both daily and yearly scale. Overall, this study was an attempt to integrate N into the LUE model to obtain the spatiotemporally dynamic εmax while simultaneously taking into account the impacts of multiple environmental variables on the GPP estimates. The proposed model has the potential for satisfactory GPP simulations on a global or regional scale.

Response of ecosystem gross primary productivity to drought in northern China based on multi-source remote sensing data

Drought has become one of the main reasons for the loss of terrestrial ecosystems gross primary productivity (GPP) because of its more frequent occurrence under the background of global warming. Aiming to explore the response mechanism of vegetation under drought conditions, the sun-induced chlorophyll fluorescence (SIF), normalized difference vegetation index (NDVI) and enhanced vegetation index (EVI) were used to monitor vegetation drought and quantify the drought-induced GPP variabilities. MODIS GPP were used for analysis, which was verified by close-path eddy covariance flux (R2 = 0.69, p < 0.01). According to the standard precipitation evapotranspiration index (SPEI) and soil moisture (SM), the drought periods of 2010 and 2014 in the eastern piedmont of Taihang Mountains were selected to analyze drought impacts on GPP. We found that SIF had the ability to reflect the effects of drought prolonged action on vegetation and respond to GPP persistent changes during drought. SIF successfully described GPP loss during droughts in 2010 (20.91 gCm−2month−1) and 2014 (9.52 gCm−2month−1). SIF was more significantly linearly correlated with GPP (R2 = 0.89) than NDVI (R2 = 0.69) and EVI (R2 = 0.84), suggesting that SIF was more sensitive to physiological changes in vegetation. SIF correlated significantly higher to SM than SPEI, and there was a two-month lag to SM, which may be related to the adaptation mechanism of vegetation physiological recovery under drought conditions. Results showed that SIF is more accurate and sensitive than NDVI and EVI in regional vegetation drought monitoring and drought-induced GPP variations. SIF is an effective physiological index for timely detection and regional drought, and an effective tool for ecosystem management and disaster warning.

Estimation of Global Cropland Gross Primary Production from Satellite Observations by Integrating Water Availability Variable in Light-Use-Efficiency Model

Satellite-based models have been widely used to estimate gross primary production (GPP) of terrestrial ecosystems. Although they have many advantages for mapping spatiotemporal variations of regional or global GPP, the performance in agroecosystems is relatively poor. In this study, a light-use-efficiency model for cropland GPP estimation, named EF-LUE, driven by remote sensing data, was developed by integrating evaporative fraction (EF) as limiting factor accounting for soil water availability. Model parameters were optimized first using CO2 flux measurements by eddy covariance system from flux tower sites, and the optimized parameters were further spatially extrapolated according to climate zones for global cropland GPP estimation in 2001–2019. The major forcing datasets include the fraction of absorbed photosynthetically active radiation (FAPAR) data from the Copernicus Global Land Service System (CGLS) GEOV2 dataset, EF from the ETMonitor model, and meteorological forcing variables from ERA5 data. The EF-LUE model was first evaluated at flux tower site-level, and the results suggested that the proposed EF-LUE model and the LUE model without using water availability limiting factor, both driven by flux tower meteorology data, explained 82% and 74% of the temporal variations of GPP across crop sites, respectively. The overall KGE increased from 0.73 to 0.83, NSE increased from 0.73 to 0.81, and RMSE decreased from 2.87 to 2.39 g C m−2 d−1 in the estimated GPP after integrating EF in the LUE model. These improvements may be largely attributed to parameters optimized for different climatic zones and incorporating water availability limiting factor expressed by EF into the light-use-efficiency model. At global scale, the verification by GPP measurements from cropland flux tower sites showed that GPP estimated by the EF-LUE model driven by ERA5 reanalysis meteorological data and EF from ETMonitor had overall the highest R2, KGE, and NSE and the smallest RMSE over the four existing GPP datasets (MOD17 GPP, revised EC-LUE GPP, GOSIF GPP and PML-V2 GPP). The global GPP from the EF-LUE model could capture the significant negative GPP anomalies during drought or heat-wave events, indicating its ability to express the impacts of the water stress on cropland GPP.

Daily estimation of gross primary production under all sky using a light use efficiency model coupled with satellite passive microwave measurements

Although satellite-based light use efficiency (LUE) model is widely used to estimate gross primary production (GPP) of terrestrial ecosystems, microwave observations have not been integrated into LUE models. This study developed a new LUE model coupled with a passive microwave vegetation index (Emissivity Difference Vegetation Index, EDVI) for daily GPP estimation. Normalized EDVI (nEDVI), an indicator of canopy-scale leaf development and biomass change, was used as a proxy of fraction of photosynthetically active radiation (FPAR). EDVI-based evaporative fraction (EDVI-EF) under all sky was used to indicate synoptic-scale water stress on LUE. 8-year in-situ measurements from seven flux tower sites (four forests, two grasslands and one croplands) of ChinaFLUX network were used to evaluate the model. We found that nEDVI-based FPAR better captured the short-term variations in daily in-situ GPP (GPPobs) than other optic-based FPAR schemes. This capability of nEDVI was more noticeable under moderate and heavy cloud cover (Frc) conditions. Validations against daily GPPobs at all sites showed that EDVI-based GPP (GPPEDVI) generated an overall small bias of −0.47 gC m−2 day−1 (−8.1%) and good Taylor score (S) of 0.86 at the daily scale. Better accuracy of GPPEDVI was found at forests sites with R2 of 0.43 to 0.73, bias of-5.29% to 3.03% and S of 0.58 to 0.78, respectively. At a tropical forest site with most frequent cloud cover, the model also well captured the variation in GPPobs from clear sky to cloudy sky (R2 of 0.93) with stable accuracies. Furthermore, the accuracy of daily GPPEDVI was found to be comparable with global satellite optic MOD17 GPP (GPPMOD17) and EC-LUE GPP (GPPECLUE) from 8-day to yearly scales across the sites. In particular, GPPEDVI performed generally smaller bias at evergreen broadleaf forests, while both of GPPMOD17 and GPPECLUE were overestimated, suggesting that there could be less saturation for microwave-based LUE model over dense vegetation. Although all three satellite LUE models severely underestimated GPP of crop, GPPEDVI generated lower bias (−29.8%) than GPPMOD17 (−66.8%) and GPPECLUE (−59.5%). Overall, this study is the first attempt toward the integration of microwave-derived variables into LUE model for daily GPP estimation. The microwave-based LUE model has a potential of mapping spatiotemporally continues daily GPP under various clouds.

VODCA2GPP - A new global, long-term (1988-2020) GPP dataset from microwave remote sensing

Abstract. Long-term global monitoring of terrestrial Gross Primary Production (GPP) is crucial for assessing ecosystem response to global climate change. In recent years and decades, great advances in estimating GPP on a global level have been made and many global GPP datasets have been published. These global data records are either based on observations from optical remote sensing, are upscaled from in situ measurements, or rely on process-based models. The different estimation approaches are well established within the scientific community but also exhibit significant discrepancies among each other. Here, we introduce the new VODCA2GPP dataset, which utilizes microwave remote sensing estimates of Vegetation Optical Depth (VOD) to estimate GPP on a global scale. VODCA2GPP is able to complement existing products with long-term GPP estimates covering the period 1988–2020. VODCA2GPP applies a previously developed carbon sink-driven approach (Teubner et al., 2019, 2021) to estimate GPP from the Vegetation Optical Depth Climate Archive (Zotta et al., in prep.; Moesinger et al., 2020), which merges VOD observations from multiple sensors into one long-running, coherent data record. VODCA2GPP was trained and evaluated against FLUXNET in situ observations of GPP and assessed against largely independent state-of-the art GPP datasets (MODIS GPP, FLUXCOM GPP, and GPP estimates from the TRENDY-v7 model ensemble). These assessments show that VODCA2GPP exhibits very similar spatial patterns compared to existing GPP datasets across all biomes but with a consistent positive bias. In terms of temporal dynamics, a high agreement was found for regions outside the humid tropics, with median correlations around 0.75. Concerning anomalies from the long-term climatology, VODCA2GPP correlates well with MODIS and TRENDY-v7 GPP (Pearson’s r: 0.53 and 0.61) but less with FLUXCOM GPP (Pearson’s r: 0.29). A trend analysis for the period 1988–2019 did not exhibit a significant trend in VODCA2GPP on a global scale but rather suggests regionally differing long-term changes in GPP. Significant similar increases of global GPP that were found for VODCA2GPP, MODIS GPP, and the TRENDY-v7 ensemble for the shorter overlapping observation period (2003–2015) supports the theory of elevated CO2 uptake potentially induced by increased atmospheric CO2 concentrations and the associated rising temperatures. The VODCA2GPP dataset is available at TU Data ( https://doi.org/10.48436/1k7aj-bdz35 ; Wild et al., 2021).

Tidal Wetland Gross Primary Production Across the Continental United States, 2000–2019

AbstractWe mapped tidal wetland gross primary production (GPP) with unprecedented detail for multiple wetland types across the continental United States (CONUS) at 16‐day intervals for the years 2000–2019. To accomplish this task, we developed the spatially explicit Blue Carbon (BC) model, which combined tidal wetland cover and field‐based eddy covariance tower data into a single Bayesian framework, and used a super computer network and remote sensing imagery (Moderate Resolution Imaging Spectroradiometer Enhanced Vegetation Index). We found a strong fit between the BC model and eddy covariance data from 10 different towers (r2 = 0.83, p &lt; 0.001, root‐mean‐square error = 1.22 g C/m2/day, average error was 7% with a mean bias of nearly zero). When compared with NASA's MOD17 GPP product, which uses a generalized terrestrial algorithm, the BC model reduced error by approximately half (MOD17 had r2 = 0.45, p &lt; 0.001, root‐mean‐square error of 3.38 g C/m2/day, average error of 15%). The BC model also included mixed pixels in areas not covered by MOD17, which comprised approximately 16.8% of CONUS tidal wetland GPP. Results showed that across CONUS between 2000 and 2019, the average daily GPP per m2 was 4.32 ± 2.45 g C/m2/day. The total annual GPP for the CONUS was 39.65 ± 0.89 Tg C/year. GPP for the Gulf Coast was nearly double that of the Atlantic and Pacific Coasts combined. Louisiana alone accounted for 15.78 ± 0.75 Tg C/year, with its Atchafalaya/Vermillion Bay basin at 4.72 ± 0.14 Tg C/year. The BC model provides a robust platform for integrating data from disparate sources and exploring regional trends in GPP across tidal wetlands.

Multi‐model analysis of climate impacts on plant photosynthesis in China during 2000–2015

AbstractDifferences, arising from differences in gross primary production (GPP) model structures and driving forces, have fuelled arguments concerning interannual changes of GPP in China since 2000. To better investigate the interannual variability of GPP and its covariance with climate factors in China, this study adopted a multi‐model analysis based on three GPP models (i.e., Terrestrial Ecosystem Carbon flux model [TEC], Breathing Earth System Simulator model [BESS], and MOD17 GPP model). The results show that annual GPP in China increased by 0.021–0.057 Pg C year−1 from 2000 to 2015 attributable to atmospheric‐CO2 fertilization effects and favourable climate change, that is, increasing precipitation (Pr) and temperature (Ta). However, northern China and southern China had a large difference in the amplitude of these GPP changes; annual GPP increased by 0.017–0.039 Pg C year−1 in northern China but only 0.001–0.018 Pg C year−1 in southern China. Northern China and southern China occupy contrasting climate zones and this contrast produced different interannual variability of GPP through different mechanisms. Northern China has a dry climate with GPP changes sensitive to Pr. As a result, more Pr along with higher Ta in northern China produced the strong uptrend of GPP from 2000 to 2015. In contrast, southern China has a wet climate with its GPP sensitive to solar radiation and Ta. For the interval of 2000–2015, decreasing radiation plus drought exerted a negative influence on GPP in southern China. This study highlights the diverse mechanisms in which climate change affects GPP in dry and wet climate zones. A robust multi‐model analysis is preferred to reduce uncertainties arising from a single GPP model and its driving data.

Improvement of satellite-based estimation of gross primary production through optimization of meteorological parameters and high resolution land cover information at regional scale over East Asia

Gross primary production (GPP) is a crucial factor in the carbon cycle especially for the absorption of carbon dioxide into the biosphere from the atmosphere. A large discrepancy between a satellite-based GPP product named MOD17A2H GPP and in-situ data measured at eddy covariance flux towers has been identified over East Asia where the biome types and climatic characteristics are heterogeneous with rugged terrain. For that reason, this study focuses on two potential major error sources in MOD17A2H GPP, which are the coarse resolution land cover information and inappropriate meteorological parameters. The finer resolution observation and monitoring global land cover (FROM-GLC) and the MODIS land cover product collection 5.1 (MCD12Q1) were used to describe biome types in detail, by combining spatial distribution from FROM-GLC and the phenological characteristics of land cover from MCD12Q1. Meteorological parameters were optimized using the 55-years Japanese reanalysis meteorological data (JRA-55). The light use efficiency of the MOD17 GPP algorithm was modified using the combined land cover information (FROM-MCD). Although the use of FROM-MCD and JRA-55 did not improve MOD17A2H GPP, the optimization of two meteorological parameters—daily minimum air temperature (TMIN) and vapor pressure deficit (VPD) significantly improved the GPP algorithm for East Asia. The results show that the root mean square errors (RMSEs) between the estimated and in situ GPPs decreased from 21.83 (gC/m2/8days) to 16.11 (gC/m2/8days) through optimizing the two parameters at 9 flux tower sites. The optimized TMIN and VPD thresholds in the MOD17 GPP algorithm were applied to the entire study area (i.e., East Asia) according to the Köppen-Geiger climate classes. The estimated GPP using the proposed approach was compared to GPPs from widely used process-based models (i.e., VISIT, BEAMS, and BESS), which confirmed that the proposed approach with locally optimized meteorological parameters improved on the underestimation of the MOD17 GPP algorithm for East Asia. The uncertainty of the VPDmin parameter was revealed to be larger than that of TMINmax.

Spatiotemporal Consistency of Four Gross Primary Production Products and Solar‐Induced Chlorophyll Fluorescence in Response to Climate Extremes Across CONUS in 2012

AbstractLarge spatial‐scale effects of climate extremes on gross primary production (GPP), the largest terrestrial carbon flux, are highly uncertain even as these extremes increase in frequency and extent. Here we report the impacts of spring warming and summer drought in 2012 on GPP across the contiguous United States (CONUS) using estimates from four GPP models: Vegetation Photosynthesis Model (VPM), MOD17A2H V006, Carnegie‐Ames‐Stanford Approach, and Simple Biosphere/Carnegie‐Ames‐Stanford Approach. VPM simulations are driven by Moderate Resolution Imaging Spectroradiometer, North American Regional Reanalysis climate data, and C3 and C4 cropland maps from the United States Department of Agriculture Cropland Data Layer data set. Across 25 eddy covariance flux tower sites, GPP estimates from VPM (GPPVPM) showed better accuracy in terms of cross‐site variability and interannual variability (R2 = 0.84 and 0.46, respectively) when compared to MOD17 GPP. We further assessed the spatial and temporal (seasonal) consistency between GPP products and the Global Ozone Monitoring Experiment‐2 solar‐induced chlorophyll fluorescence over CONUS during 2008–2014. The results suggested that GPPVPM agrees best with solar‐induced chlorophyll fluorescence across space and time, capturing seasonal dynamics and interannual variations. Anomaly analyses showed that increased GPP during the spring compensated for the reduced GPP during the summer, resulting in near‐neutral changes in annual GPP for the CONUS. This study demonstrates the importance of assessing the impacts of different types and timing of climate extremes on GPP and the need to improve light use efficiency models by incorporating C3 and C4 plant functional types.

Correction: Evaluating the role of land cover and climate uncertainties in computing gross primary production in Hawaiian Island ecosystems.

[This corrects the article DOI: 10.1371/journal.pone.0184466.].

Evaluating the role of land cover and climate uncertainties in computing gross primary production in Hawaiian Island ecosystems.

Gross primary production (GPP) is the Earth’s largest carbon flux into the terrestrial biosphere and plays a critical role in regulating atmospheric chemistry and global climate. The Moderate Resolution Imaging Spectrometer (MODIS)-MOD17 data product is a widely used remote sensing-based model that provides global estimates of spatiotemporal trends in GPP. When the MOD17 algorithm is applied to regional scale heterogeneous landscapes, input data from coarse resolution land cover and climate products may increase uncertainty in GPP estimates, especially in high productivity tropical ecosystems. We examined the influence of using locally specific land cover and high-resolution local climate input data on MOD17 estimates of GPP for the State of Hawaii, a heterogeneous and discontinuous tropical landscape. Replacing the global land cover data input product (MOD12Q1) with Hawaii-specific land cover data reduced statewide GPP estimates by ~8%, primarily because the Hawaii-specific land cover map had less vegetated land area compared to the global land cover product. Replacing coarse resolution GMAO climate data with Hawaii-specific high-resolution climate data also reduced statewide GPP estimates by ~8% because of the higher spatial variability of photosynthetically active radiation (PAR) in the Hawaii-specific climate data. The combined use of both Hawaii-specific land cover and high-resolution Hawaii climate data inputs reduced statewide GPP by ~16%, suggesting equal and independent influence on MOD17 GPP estimates. Our sensitivity analyses within a heterogeneous tropical landscape suggest that refined global land cover and climate data sets may contribute to an enhanced MOD17 product at a variety of spatial scales.

Evaluation of MODIS-based estimates of water-use efficiency in Amazonia

ABSTRACTThis study aimed to assess the spatial-temporal patterns of water-use efficiency (WUE) obtained through MODIS gross primary productivity (GPP) and evapotranspiration (ET) products (MOD17 for GPP and MOD16 for ET) in the Upper Tapajos and Curua-Una River basins, located in the oriental flank of the Amazon region, and to validate the results with flux tower measurements within the Large-Scale Biosphere-Atmosphere Experiment in Amazonia (LBA) project. The spatial variation of WUE was primarily related to the larger presence of forested areas in the Upper Tapajos River basin (western part) compared with the Curua-Una River basin (eastern part), which is situated within the so-called arc of deforestation. Temporally, WUE showed a pronounced seasonal pattern, varying with the dry and wet seasons in the region. A decrease of ~3% in WUE was observed during the dry season, which was related to the low water availability and increased vapour pressure deficit during the dry period, which induces stomatal closure, leading to a decline in the photosynthetic rate. Comparison between the WUE estimates obtained by MODIS data and observations from the LBA towers showed an average error of 17%, varying between ~12% and ~28% for the different sites. MODIS WUE depends on the accuracy of both GPP and ET estimation. In this sense, we highlight that improvements in both MODIS GPP and ET products are necessary and should focus on reducing the uncertainties related to the biophysical vegetation parameters and meteorological data that serve as input information in the algorithms.

Parameterizing ecosystem light use efficiency and water use efficiency to estimate maize gross primary production and evapotranspiration using MODIS EVI

Abstract Quantifying global carbon and water balances requires accurate estimation of gross primary production (GPP) and evapotranspiration (ET), respectively, across space and time. Models that are based on the theory of light use efficiency (LUE) and water use efficiency (WUE) have emerged as efficient methods for predicting GPP and ET, respectively. Currently, LUE and WUE estimates are obtained from biome-specific look-up tables and coarse resolution remote sensing data with large uncertainties. The major objective of this study was to parameterize eddy covariance tower-based ecosystem LUE (ELUEEC), defined as the ratio of tower-based GPP (GPPEC) to photosynthetically active radiation (PAR), and ecosystem WUE (EWUEEC), defined as the ratio of GPPEC to tower-based ET (ETEC), using the Moderate Resolution Imaging Spectroradiometer (MODIS)-derived enhanced vegetation index (EVI) for predicting maize (Zea mays L.) GPP and ET, respectively. Three adjacent AmeriFlux maize sites with different rotations (continuous maize vs. annual rotation of maize and soybean, Glycine max L.) and water management practices (rainfed vs. irrigated) located near Mead, NE, USA were selected. The EVI tracked the seasonal variations of ELUEEC (R2 = 0.83) and EWUEEC (R2 = 0.74) across sites, indicating that EVI can be explicitly used as a measure of ELUEEC and EWUEEC. The predicted GPP (GPPELUE) using the parameterized ELUE model correlated well with GPPEC (slope = 1.0, R2 = 0.83, and RMSE = 2.85 g C m−2 d−1) and was significantly improved when compared to widely used models that estimate GPP by integrating EVI and climate variables (Greenness and Radiation, Temperature and Greenness, and Vegetation Index) and the standard MOD17 GPP product. Similarly, the predicted ET (ETEWUE) using the parameterized EWUE correlated well with ETEC (slope = 1.02, R2 = 0.62, and RMSE = 0.83 mm ET−1) and was significantly improved when compared to the standard MOD16 ET product. Preliminary data demonstrate that ELUE and EWUE can be parameterized using EVI, offering new methods for predicting GPP and ET.

Contrasting changes in gross primary productivity of different regions of North America as affected by warming in recent decades

Ecosystem responses to the increasing warming in recent decades across North America (NA) are spatially heterogeneous and partly uncertain. Here we examined the spatial and temporal variability of warming across different eco-regions of NA using long-term (1979–2010) climate data (North America Regional Reanalysis (NARR)) with 3-hourly time-step and 0.25°×0.25° spatial resolution and run a comprehensive mathematical process model, ecosys to study the impacts of this variability in warming on gross primary productivity (GPP). In a site scale test of model results, annual GPP modeled for pixels which corresponded to the locations of 20 eddy covariance flux towers correlated well (R2=0.76) with annual GPP derived from the towers in 2005. At continental scale, long-term annual average modeled GPP correlated well (geographically weighed regression R2=0.8) with MODIS GPP. GPP modeled in eastern temperate forests and most areas with lower mean annual air temperature (Ta), such as those in northern forests and Taiga, increased due to early spring and late autumn warming observed in NARR and these eco-regions contributed 92% of the increases in NA GPP over the last three decades. However, modeled GPP declined in most southwestern regions of NA (accounting >50% of the ecosystems with declining GPP), due to water stress from rising Ta and declining precipitation, implying that further warming and projected dryness in this region could further reduce NA carbon uptake. Overall, NA modeled GPP increased by 5.8% in the last 30 years, with a positive trend of +0.012±0.01PgCyr−1 and a range of −1.16 to +0.87PgCyr−1 caused by interannual variability of GPP from the long-term (1980–2010) mean. This variability was the greatest in southwest of US and part of the Great Plains, which could be as a result of frequent El Niño–Southern Oscillation’ events that led to major droughts.

Evaluation of Water Use Efficiency Derived from MODIS Products against Eddy Variance Measurements in China

Water use efficiency (WUE) is a useful indicator to illustrate the interaction of carbon and water cycles in terrestrial ecosystems. MODIS gross primary production (GPP) and evapotranspiration (ET) products have been used to analyze the spatial and temporal patterns of WUE and their relationships with environmental factors at regional and global scales. Although MODIS GPP and ET products have been evaluated using eddy covariance flux measurements, the accuracy of WUE estimated from MODIS products has not been well quantified. In this paper, we evaluated WUE estimated from MODIS GPP and ET products against eddy covariance measurements of GPP and ET during 2003–2008 at eight sites of the Chinese flux observation and research network (ChinaFLUX) and conducted sensitivity analysis to investigate the possible key contributors to the bias of MODIS products. Results show that MODIS products underestimate eight-day water use efficiency in four forest ecosystems and one cropland ecosystem with the bias from −0.36–−2.28 g·C·kg−1 H2O, while overestimating it in three grassland ecosystems with the bias from 0.26–1.11 g·C·kg−1 H2O. Mean annual WUE was underestimated by 14%–54% at four forest sites, 45% at one cropland site and 7% at an alpine grassland site, but overestimated by 66% and 9% at a temperate grassland site and an alpine meadow site, respectively. The underestimation of WUE by MODIS data results from underestimated GPP and overestimated ET at four forest sites, while MODIS WUE values are significantly overvalued mainly due to underestimated ET in the three grassland ecosystems. The maximum light use efficiency and fraction of photosynthetically-active radiation (FPAR) were the two most sensitive factors to the estimation of WUE derived from the MODIS GPP and ET algorithms. The error in meteorological data partly caused the overestimation of ET and accordingly underestimation in WUE in subtropical and tropical forests. The bias of MODIS-produced WUE was also derived from the uncertainties in eddy flux data due to gap-filling processes and unbalanced surface energy issue. Their contributions to the uncertainty in estimated WUE at both eight-day and annual scales still need to be further quantified.

The Performances of MODIS-GPP and -ET Products in China and Their Sensitivity to Input Data (FPAR/LAI)

The aims are to validate and assess the performances of MODIS gross primary production (MODIS-GPP) and evapotranspiration (MODIS-ET) products in China’s different land cover types and their sensitivity to remote sensing input data. In this study, MODIS-GPP and -ET are evaluated using flux derived/measured data from eight sites of ChinaFLUX. Results show that MODIS-GPP generally underestimates GPP (R2 is 0.58, bias is −6.7 gC/m2/8-day and RMSE is 19.4 gC/m2/8-day) at all sites and MODIS-ET overestimates ET (R2 is 0.36, bias is 6 mm/8-day and RMSE is 11 mm/8-day) when comparing with derived GPP and measured ET, respectively. For evergreen forests, MODIS-GPP gives a poor performance with R2 varying from 0.03 to 0.44; in contrast, MODIS-ET provides more reliable results. In croplands, MODIS-GPP can explain 80% of GPP variance, but it overestimates flux derived GPP in non-growing season and underestimates flux derived GPP in growing season; similar overestimations also presented in MODIS-ET. For grasslands and mixed forests, MODIS-GPP and -ET perform good estimating accuracy. By designing four experimental groups and taking GPP simulation as an example, we suggest that the maximum light use efficiency of croplands should be optimized, and the differences of meteorological data have little impact on GPP estimation, whereas remote sensing leaf area index/fraction of photo-synthetically active radiation (LAI/FPAR) can greatly affect GPP/ET estimations for all land cover types. Thus, accurate remote sensing parameters are important for achieving reliable estimations.