Tower-based Gross Primary Productivity Research Articles

Joint improvement on absorbed photosynthetically active radiation and intrinsic quantum yield efficiency algorithms in the P model betters the estimate of terrestrial gross primary productivity

Terrestrial gross primary productivity (GPP) plays a crucial role in global carbon cycle and budget. A range of light use efficiency (LUE) models have been developed to estimate GPP at different spatial scales. However, large uncertainties still remain in GPP output from such models, mainly owing to the difficulty in the proper determination of maximum light use efficiency (LUEmax). The recently developed P model directly quantifies actual LUE based on environmental and physiological factors, reducing the uncertainty in GPP estimation caused by the ambiguous LUEmax. However, the existing P models still suffer from a potential underestimation in global validation, which might be related to the calculation of absorbed photosynthetically active radiation (APAR) and intrinsic quantum yield efficiency (φ0) in the P model. In this study, we improved the P model by differentiating sunlit and shaded leaves for APAR calculation and by considering the spatial variability of the optimal temperature for φ0 (Topt_phi0). The roles of modified APAR and φ0 calculations in reducing the uncertainty of simulated GPP were assessed through model simulation experiments with monthly tower-based GPP from 120 flux sites globally distributed as the benchmark. The validation indicates that simultaneous improvements on APAR and φ0 algorithms are able to better the performance of the P model. Monthly GPP simulated with this improved P model (i.e., IP model) is in good agreement with tower-based values, with a linear R2, root mean squared error, mean error, mean absolute error, and DISO of 0.76, 1.88 g C m−2d−1, -0.14 g C m−2d−1, 1.77 g C m−2d−1 and 0.60. The total global terrestrial GPP output from the IP model (IPGPP) averaged 134.3 Pg C yr−1 during 1981—2021, in the upper range of existing GPP products.

CMLR: A Mechanistic Global GPP Dataset Derived from TROPOMIS SIF Observations

Solar-induced chlorophyll fluorescence (SIF) has shown promise in estimating gross primary production (GPP); however, there is a lack of global GPP datasets directly utilizing SIF with models possessing clear expression of the biophysical and biological processes in photosynthesis. This study introduces a new global 0.05° SIF-based GPP dataset (CMLR GPP, based on Canopy-scale Mechanistic Light Reaction model) using TROPOMI observations. A modified mechanistic light response model was employed at the canopy scale to generate this dataset. The canopy q L (opened fraction of photosynthesis II reaction centers), required by the CMLR model, was parameterized using a random forest model. The CMLR GPP estimates showed a strong correlation with tower-based GPP ( R 2 = 0.72) in the validation dataset, and it showed comparable performance with other global datasets such as Boreal Ecosystem Productivity Simulator (BEPS) GPP, FluxSat GPP, and GOSIF (global, OCO-2-based SIF product) GPP at a global scale. The high accuracy of CMLR GPP was consistent across various normalized difference vegetation index, vapor pressure deficit, and temperature conditions, as well as different plant functional types and most months of the year. In conclusion, CMLR GPP is a novel global GPP dataset based on mechanistic frameworks, whose availability is expected to contribute to future research in ecological and geobiological regions.

Improved Modeling of Gross Primary Production and Transpiration of Sugarcane Plantations with Time-Series Landsat and Sentinel-2 Images

Sugarcane croplands account for ~70% of global sugar production and ~60% of global ethanol production. Monitoring and predicting gross primary production (GPP) and transpiration (T) in these fields is crucial to improve crop yield estimation and management. While moderate-spatial-resolution (MSR, hundreds of meters) satellite images have been employed in several models to estimate GPP and T, the potential of high-spatial-resolution (HSR, tens of meters) imagery has been considered in only a few publications, and it is underexplored in sugarcane fields. Our study evaluated the efficacy of MSR and HSR satellite images in predicting daily GPP and T for sugarcane plantations at two sites equipped with eddy flux towers: Louisiana, USA (subtropical climate) and Sao Paulo, Brazil (tropical climate). We employed the Vegetation Photosynthesis Model (VPM) and Vegetation Transpiration Model (VTM) with C4 photosynthesis pathway, integrating vegetation index data derived from satellite images and on-ground weather data, to calculate daily GPP and T. The seasonal dynamics of vegetation indices from both MSR images (MODIS sensor, 500 m) and HSR images (Landsat, 30 m; Sentinel-2, 10 m) tracked well with the GPP seasonality from the EC flux towers. The enhanced vegetation index (EVI) from the HSR images had a stronger correlation with the tower-based GPP. Our findings underscored the potential of HSR imagery for estimating GPP and T in smaller sugarcane plantations.

Synergy between TROPOMI sun-induced chlorophyll fluorescence and MODIS spectral reflectance for understanding the dynamics of gross primary productivity at Integrated Carbon Observatory System (ICOS) ecosystem flux sites

Abstract. An accurate estimation of vegetation gross primary productivity (GPP), which is the amount of carbon taken up by vegetation through photosynthesis for a given time and area, is critical for understanding terrestrial–atmosphere CO2 exchange processes and ecosystem functioning, as well as ecosystem responses and adaptations to climate change. Prior studies, based on ground, airborne, and satellite sun-induced chlorophyll fluorescence (SIF) observations, have recently revealed close relationships with GPP at different spatial and temporal scales and across different plant functional types (PFTs). However, questions remain regarding whether there is a unique relationship between SIF and GPP across different sites and PFTs and how we can improve GPP estimates using solely remotely sensed data. Using concurrent measurements of daily TROPOspheric Monitoring Instrument (TROPOMI) SIF (daily SIFd); daily MODIS Terra and Aqua spectral reflectance; vegetation indices (VIs, notably normalized difference vegetation index (NDVI), near-infrared reflectance of vegetation (NIRv), and photochemical reflectance index (PRI)); and daily tower-based GPP across eight major different PFTs, including mixed forests, deciduous broadleaf forests, croplands, evergreen broadleaf forests, evergreen needleleaf forests, grasslands, open shrubland, and wetlands, the strength of the relationships between tower-based GPP and SIFd at 40 Integrated Carbon Observation System (ICOS) flux sites was investigated. The synergy between SIFd and MODIS-based reflectance (R) and VIs to improve GPP estimates using a data-driven modeling approach was also evaluated. The results revealed that the strength of the hyperbolic relationship between GPP and SIFd was strongly site-specific and PFT-dependent. Furthermore, the generalized linear model (GLM), fitted between SIFd, GPP, and site and vegetation type as categorical variables, further supported this site- and PFT-dependent relationship between GPP and SIFd. Using random forest (RF) regression models with GPP as output and the aforementioned variables as predictors (R, SIFd, and VIs), this study also showed that the spectral reflectance bands (RF-R) and SIFd plus spectral reflectance (RF-SIF-R) models explained over 80 % of the seasonal and interannual variations in GPP, whereas the SIFd plus VI (RF-SIF-VI) model reproduced only 75 % of the tower-based GPP variance. In addition, the relative variable importance of predictors of GPP demonstrated that the spectral reflectance bands in the near-infrared, red, and SIFd appeared as the most influential and dominant factors determining GPP predictions, indicating the importance of canopy structure, biochemical properties, and vegetation functioning on GPP estimates. Overall, this study provides insights into understanding the strength of the relationships between GPP and SIF and the use of spectral reflectance and SIFd to improve estimates of GPP across sites and PFTs.

Simulation of forest carbon fluxes by integrating remote sensing data into biome-BGC model

The accurate quantification and monitoring of intra- and inter-annual variability of long-term forest carbon fluxes are imperative to understand the changes in the forest ecosystems in response to the changing climate. The present study aims to simulate forest carbon fluxes in two major plant functional types (PFTs) of northwest Himalayan (NWH) foothills of India by integrating time-series remote sensing (RS) data into Biome-Biogeochemical Cycle (Biome-BGC), a process-based model, and to study the spatio-temporal variability of carbon fluxes. The parameterization of the Biome-BGC model was carried out using the data from two Eddy covariance (EC) flux tower sites located in the NWH foothills of India. An analysis was carried out to identify the sensitive parameters and their calibration was performed. The calibrated Biome-BGC model with a higher coefficient of determination (R2) and%RMSE for moist deciduous (R2 = 0.80, %RMSE = 13.24) and dry deciduous (R2 = 0.79, %RMSE = 13.85) PFTs was used to estimate the gross primary productivity (GPP) from 2001 to 2018. However, the range of model-simulated leaf area index (LAI) was found to be lower than the field observed LAI. Integrating satellite-based Global Land Surface Satellite (GLASS) LAI into the Biome-BGC model led to substantial improvement in GPP estimates of moist deciduous (R2 = 0.87, %RMSE = 11.12) and dry deciduous PFTs (R2 = 0.86, %RMSE = 10.38) when compared to EC tower-based GPP for the year 2018. Based upon the accuracy, GLASS LAI integrated Biome-BGC model was used to map the spatio-temporal variability of forest carbon fluxes. The study highlighted that integration of RS data into calibrated process-based model increased the accuracy of model-simulated forest carbon fluxes.

Fine resolution remote sensing spectra improves estimates of gross primary production of croplands

Gross primary production (GPP) is a fundamental measure of the terrestrial carbon cycle critical to our understanding of ecosystem function under the changing climate and land use. Remote sensing enables access to continuous spatial coverage, but remains challenged in heterogeneous croplands. Coarse resolution products, like MOD17A (500 m), may aggregate fragmented land cover types commonly found in heavily managed landscapes and misrepresent their respective contribution to carbon production. Consequently, this study demonstrates the capability of fine-resolution imagery (20-30 m) and available red-edge vegetation indices to characterize GPP across seven Midwest cropping systems. Four sites were established on a 22-year-old USDA Conservation Reserve Program (CRP); and the other three on land conventionally farmed with corn-soybean-wheat rotation (AGR). We compare in situ GPP estimates from eddy-covariance towers with ten satellite models: eight variants of the vegetation photosynthesis models (VPM), of which five include a red-edge vegetation index, as well as conventional products Landsat CONUS GPP (30 m) and MOD17A2H V6 (500 m). Daily and cumulative fine-resolution imagery integrated within VPM generally agreed with tower-based GPP in heterogeneous landscapes more than those from MODIS 500 m VPM or conventional GPP products from MOD17AH V6 or Landsat 8 CONUS. Replacing EVI2 with red-edge indices NDRE2, NDRE1, and MTCI in Sentinel 2 VPMs notably improved explanation of variance and estimation of cumulative GPP. While existing methods using MODIS- and Landsat-derived GPP are important baselines for regional and global studies, future research may benefit from the higher spatial, temporal, and radiometric resolution.

Modeling Carbon Uptake of Dryland Maize Using High Resolution Satellite Imagery

Quantifying carbon uptake or gross primary production (GPP) from agroecosystems is important for understanding the spatial and temporal dynamics of carbon fixation by crops. The availability of high-resolution remote sensing data can significantly improve GPP estimation of small-scale agricultural fields. Multispectral satellite data with 3-m spatial resolution and frequent global coverage are available from the PlanetScope network of satellites. However, this data remains largely unexplored for studying the carbon dynamics of agroecosystems. The overarching goal of this study was to develop a simple empirical method for quantifying the GPP of dryland maize (Zea mays L.) using remotely sensed vegetation indices along with in-situ measurements of photosynthetically active radiation and leaf area index by linking it with carbon uptake data from an eddy covariance flux tower. Four vegetation indices were investigated: the normalized difference vegetation index (NDVI), the soil adjusted vegetation index (SAVI), the weighted difference vegetation index (WDVI), and the two-band enhanced vegetation index (EVI2). This study was conducted over a three-year period from 2017 to 2019 in East-Central Texas. A total of 12 GPP prediction models were developed using individual yearly data and were used for predicting GPP of the other 2 years. Predicted maize GPP values were then compared against tower-based GPP. The NDVI models were the least successful in predicting GPP and had the highest root mean square error (average: 10.1 3 gC m−2; maximum: 26.3 gC m−2). Models based on SAVI performed especially well with error ranging from 0.05 to 0.94 gC m−2. The slope of the regression between SAVI-based estimated GPP and measured GPP was not different from 1.0 in all combinations of years. The success of the SAVI-based GPP models for predicting dryland maize carbon uptake indicates that it was the least affected vegetation index by changing soil background condition in this row cropping system.

Remotely Monitoring Vegetation Productivity in Two Contrasting Subtropical Forest Ecosystems Using Solar-Induced Chlorophyll Fluorescence

Subtropical forests can sequester a larger amount of atmospheric carbon dioxide (CO2) relative to other terrestrial ecosystems through photosynthetic activity and act as an important role in mitigating global climate warming. Compared with the model-based gross primary production (GPP) products, satellite-derived solar-induced fluorescence (SIF) opens a new window for quantification. Here, we used the remotely sensed SIF retrievals, two satellite-driven GPP products including MODIS (GPPMOD) and BESS (GPPBESS), and tower-based GPP measurements at two contrasting subtropical forests to provide a systematic analysis. Our results revealed that GPP and the associated environmental factors exhibited distinct seasonal patterns. However, the peak GPP values had large differences, with stronger GPP in the evergreen needleleaf forest site (8.76 ± 0.71 g C m−2 d−1) than that in the evergreen broadleaf forest site (5.71 ± 0.31 g C m−2 d−1). The satellite-derived SIF retrievals showed great potential in quantifying the variability in GPP, especially for the evergreen needleleaf forest with r reaching up to 0.909 (p < 0.01). GPPMOD and GPPBESS showed distinctly different performances for the two subtropical forests, whereas the GPP estimates by exclusive use of satellite-based SIF data promised well to the tower-based GPP observations. Multi-year evaluation again confirmed the good performance of the SIF-based GPP estimates. These findings will provide an alternative framework for quantifying the magnitude of forest GPP and advance our understanding of the carbon sequestration capacity of subtropical forest ecosystems.

Improving the Gross Primary Productivity Estimate by Simulating the Maximum Carboxylation Rate of the Crop Using Machine Learning Algorithms

The current regional-scale process-based photosynthesis models use biome-specified values of maximum carboxylation rate at 25 &#x00B0;C (<i>V</i>_<i>m</i>25) in simulating ecosystem gross primary productivity (GPP). These models ignore the variations in <i>V</i>_<i>m</i>25 over time and space, resulting in substantial errors in regional estimates of cropland GPP. Thus, to resolve this problem, we used the ensemble Kalman filter (EnKF) to assimilate tower-based GPP from five maize flux sites into a process-based mode to obtain the &#x201C;apparent&#x201D; value of <i>V</i>_<i>m</i>25 and then modeled this parameter using machine learning (ML) algorithms. The results showed that <i>V</i>_<i>m</i>25 increased during the early growing season and then decreased after reaching a peak value in the middle of the growing season. The coefficient of determination (<i>R</i>²) (root mean square error, RMSE) for SCOPES-Crop with EnKF-derived varied <i>V</i>_<i>m</i>25 in simulating daily GPP across all site-days increased (decreased) by 0.17 (5.63 &#x03BC;<i>mol m</i>^-2 <i>s</i>^-1) on average compared to that for the model with fixed <i>V</i>_<i>m</i>25. We used four ML algorithms, namely artificial neural network, random forest, extreme gradient enhancement, and convolutional neural network (CNN), to model the <i>V</i>_<i>m</i>25 of maize. The CNN algorithm yielded the best results. The average of the <i>R</i>² (RMSE) values of simulated GPP using CNN-based <i>V</i>_<i>m</i>25 over the three flux sites is 0.93 (1.95 &#x03BC;<i>mol m</i>^-2 <i>s</i>^-1), higher (smaller) than that using fixed <i>V</i>_<i>m</i>25. This study implies that representing the seasonal variations in <i>V</i>_<i>m</i>25 can facilitate improved estimates of GPP and the ML methods are useful tools for modeling the variation in <i>V</i>_<i>m</i>25.

TROPOMI observations allow for robust exploration of the relationship between solar-induced chlorophyll fluorescence and terrestrial gross primary production

Solar-induced chlorophyll fluorescence (SIF) observed by satellites has advanced the monitoring of terrestrial photosynthesis regionally and globally. The relationship between SIF and gross primary production (GPP) at leaf, canopy, and ecosystem scales has received tremendous attention in recent years. It remains controversial whether the SIF-GPP relationship at the ecosystem scale is universal or dependent upon vegetation type. New SIF observations from the TROPOspheric Monitoring Instrument (TROPOMI) with unprecedented high spatial and temporal resolution provide a new opportunity to elucidate the SIF-GPP relationship. Here, we examine the SIF-GPP relationship for seven major vegetation types across the U.S. with TROPOMI SIF and in-situ GPP data for 83 eddy covariance flux sites. We find that TROPOMI SIF shows a strong and consistent relationship with tower-based GPP at both satellite footprint and grid-cell levels. The slope of the SIF-GPP relationship is similar among all the vegetation types except croplands, demonstrating a nearly universal (converging to ~13.5 g C m−2 d−1/W m−2 μm−1 sr−1) rather than vegetation type-specific SIF-GPP relationship. This confirms that TROPOMI SIF can be used as a proxy for GPP across a wide variety of vegetation types, and can also be used to quantify GPP by avoiding uncertainty associated with land cover maps. The C4 crops have a much higher slope than the C3 crops, and therefore croplands tend to have a higher slope than C3-dominated vegetation types (e.g., forests, shrublands, savannas). We also find that the TROPOMI SIF is well correlated with GPP under normal or wetter conditions, while their relationship becomes weaker under water stress. Our TROPOMI-based study could improve our understanding of the SIF-GPP relationship at the ecosystem scale and advance the mapping of GPP globally with SIF observations from space.

Comparing Three Remotely Sensed Approaches for Simulating Gross Primary Productivity over Mountainous Watersheds: A Case Study in the Wanglang National Nature Reserve, China

Light Use Efficiency (LUE), Vegetation Index (VI)-based, and process-based models are the main approaches for spatially continuous gross primary productivity (GPP) estimation. However, most current GPP models overlook the effects of topography on the vegetation photosynthesis process. Based on the structures of a two-leaf LUE model (TL-LUE), a VI-based model (temperature and greenness, TG), and a process-based model (Boreal Ecosystem Productivity Simulator, BEPS), three models, named mountain TL-LUE (MTL-LUE), mountain TG (MTG), and BEPS-TerrainLab, have been proposed to improve GPP estimation over mountainous areas. The GPP estimates from the three mountain models have been proven to align more closely with tower-based GPP than those from the original models at the site scale, but their abilities to characterize the spatial variation of GPP at the watershed scale are not yet known. In this work, the GPP estimates from three LUE models (i.e., MOD17, TL-LUE, and MTL-LUE), two VI-based models (i.e., TG and MTG), and two process-based models (i.e., BEPS and BEPS-TerrainLab) were compared for a mountainous watershed. At the watershed scale, the annual GPP estimates from MTL-LUE, MTG, and BTL were found to have a higher spatial variation than those from the original models (increasing the spatial coefficient of variation by 6%, 8%, and 22%), highlighting that incorporating topographic information into GPP models might improve understanding of the high spatial heterogeneity of the vegetation photosynthesis process over mountainous areas. Obvious discrepancies were also observed in the GPP estimates from MTL-LUE, MTG, and BTL, with determination coefficients ranging from 0.02–0.29 and root mean square errors ranging from 399–821 gC m−2yr−1. These GPP discrepancies mainly stem from the different (1) structures of original LUE, VI, and process models, (2) assumptions associated with the effects of topography on photosynthesis, (3) input data, and (4) values of sensitive parameters. Our study highlights the importance of considering surface topography when modeling GPP over mountainous areas, and suggests that more attention should be given to the discrepancy of GPP estimates from different models.

A practical topographic correction method for improving Moderate Resolution Imaging Spectroradiometer gross primary productivity estimation over mountainous areas

Time-series Moderate Resolution Imaging Spectroradiometer (MODIS) gross primary productivity (GPP) has been served as an effective tool to assess the terrestrial carbon budget for the entire globe since 2000. However, the current MODIS GPP product neglects the surface heterogeneity in the modeling process and is always generated at 500 m or 1 km resolution, which could cause errors to these estimates over mountainous areas. In this work, the MODIS GPP model (MOD17) was applied to obtain 1 km GPP estimates at eleven mountainous sites. Then, a topographic correction method based on three indexes associated with the spatial heterogeneity of received radiation (TCIAPAR), temperature (TCITMIN), and water (TCIVPD) stresses was developed to reduce GPP errors in these MOD17-simulated estimates. Results showed that a closer relationship between tower-based GPP and MOD17-simulated GPP was achieved after applying the proposed topographic correction method, with the determination coefficient (R2) increased from 0.61 to 0.74 and root mean square error (RMSE) reduced from 24.24 to 14.56 gC m−2 8d−1 at all the eleven mountainous sites. As for the effectiveness of each topographic correction index, an obvious improvement of MOD17-simulated GPP was observed after TCIAPAR correction (increasing R2 by 0.09 and decreasing RMSE by 8.75 gC m−2 8d−1), TCITMIN correction (increasing R2 by 0.05 and decreasing RMSE by 7.80 gC m−2 8d−1), and TCIVPD correction (increasing R2 by 0.06 and decreasing RMSE by 7.89 gC m−2 8d−1), indicating that the spatial heterogeneity information of radiation, temperature, and water within coarse pixels is necessary for improving the MODIS GPP over mountainous areas. It is notable that the combination of the TCIAPAR, TCITMIN, and TCIVPD corrections was found to have the largest improvement for MOD17-simulated GPP (increasing R2 by 0.13 and decreasing RMSE by 9.68 gC m−2 8d−1), indicating that the combined consideration of topographic factors in the correction process might achieve a larger improvement. This study highlights the feasibility of incorporating surface topographic characteristics into current coarse resolution GPP products in obtaining large-scale mountain GPP estimates.

Assessment of Satellite Chlorophyll-Based Leaf Maximum Carboxylation Rate (Vcmax) Using Flux Observations at Crop and Grass Sites

The leaf maximum carboxylation rate (Vcmax) is a key parameter in modeling plant photosynthesis. The rapid and accurate acquisition of Vcmax at large scales can improve understanding of global vegetation productivity and the terrestrial carbon cycle. In this article, we assessed the retrieval of Vcmax from satellite data by validating these data using flux observations made at eight crop and grass sites. Firstly, an empirical model applicable to C3 species that was based on the semimechanistic linkage between leaf chlorophyll and Vcmax was used to derive Vcmax from satellite data. Then, using vegetation, soil, and meteorological variables as inputs, the SCOPE model was used to estimate Vcmax from half-hourly or hourly flux observations at each site. The estimates of Vcmax were assessed by comparing the simulated gross primary production (GPP) against the observed GPP: that is, the Vcmax value corresponding to its simulated diurnal GPP data with minimum root-mean-square error (RMSE) was selected as the inverted Vcmax value. Finally, the Vcmax values retrieved from MERIS and Semtinel-3 OLCI satellite data were validated using the in situ flux site observations. The results showed that the estimates of Vcmax based on satellite data successfully captured the seasonal variations in Vcmax retrieved from the tower-based GPP data, giving a mean RMSE value of 15.30 μ mol m−2 s−1. Our results support the retrieval of Vcmax from satellite data based on the link with leaf chlorophyll content and show that there was good agreement between Vcmax derived from remote sensing and flux data.

Radiance-based NIRv as a proxy for GPP of corn and soybean

Substantial uncertainty exists in daily and sub-daily gross primary production (GPP) estimation, which dampens accurate monitoring of the global carbon cycle. Here we find that near-infrared radiance of vegetation (NIRv,Rad), defined as the product of observed NIR radiance and normalized difference vegetation index, can accurately estimate corn and soybean GPP at daily and half-hourly time scales, benchmarked with multi-year tower-based GPP at three sites with different environmental and irrigation conditions. Overall, NIRv,Rad explains 84% and 78% variations of half-hourly GPP for corn and soybean, respectively, outperforming NIR reflectance of vegetation (NIRv,Ref), enhanced vegetation index (EVI), and far-red solar-induced fluorescence (SIF760). The strong linear relationship between NIRv,Rad and absorbed photosynthetically active radiation by green leaves (APARgreen), and that between APARgreen and GPP, explain the good NIRv,Rad-GPP relationship. The NIRv,Rad-GPP relationship is robust and consistent across sites. The scalability and simplicity of NIRv,Rad indicate a great potential to estimate daily or sub-daily GPP from high-resolution and/or long-term satellite remote sensing data.

Estimating crop primary productivity with Sentinel-2 and Landsat 8 using machine learning methods trained with radiative transfer simulations

Satellite remote sensing has been widely used in the last decades for agricultural applications, both for assessing vegetation condition and for subsequent yield prediction. Existing remote sensing-based methods to estimate gross primary productivity (GPP), which is an important variable to indicate crop photosynthetic function and stress, typically rely on empirical or semi-empirical approaches, which tend to over-simplify photosynthetic mechanisms. In this work, we take advantage of all parallel developments in mechanistic photosynthesis modeling and satellite data availability for an advanced monitoring of crop productivity. In particular, we combine process-based modeling with the soil-canopy energy balance radiative transfer model (SCOPE) with Sentinel-2 and Landsat 8 optical remote sensing data and machine learning methods in order to estimate crop GPP. With this approach, we by-pass the need for an intermediate step to retrieve the set of vegetation biophysical parameters needed to accurately model photosynthesis, while still accounting for the complex processes of the original physically-based model. Several implementations of the machine learning models are tested and validated using simulated and flux tower-based GPP data. Our final neural network model is able to estimate GPP at the tested flux tower sites with r2 of 0.92 and RMSE of 1.38 gC d−1 m−2, which outperforms empirical models based on vegetation indices. The first test of applicability of this model to Landsat 8 data showed good results (r2 of 0.82 and RMSE of 1.97 gC d−1 m−2), which suggests that our approach can be further applied to other sensors. Modeling and testing is restricted to C3 crops in this study, but can be extended to C4 crops by producing a new training dataset with SCOPE that accounts for the different photosynthetic pathways. Our model successfully estimates GPP across a variety of C3 crop types and environmental conditions even though it does not use any local information from the corresponding sites. This highlights its potential to map crop productivity from new satellite sensors at a global scale with the help of current Earth observation cloud computing platforms.

A Global, 0.05-Degree Product of Solar-Induced Chlorophyll Fluorescence Derived from OCO-2, MODIS, and Reanalysis Data

Solar-induced chlorophyll fluorescence (SIF) brings major advancements in measuring terrestrial photosynthesis. Several recent studies have evaluated the potential of SIF retrievals from the Orbiting Carbon Observatory-2 (OCO-2) in estimating gross primary productivity (GPP) based on GPP data from eddy covariance (EC) flux towers. However, the spatially and temporally sparse nature of OCO-2 data makes it challenging to use these data for many applications from the ecosystem to the global scale. Here, we developed a new global ‘OCO-2’ SIF data set (GOSIF) with high spatial and temporal resolutions (i.e., 0.05°, 8-day) over the period 2000–2017 based on a data-driven approach. The predictive SIF model was developed based on discrete OCO-2 SIF soundings, remote sensing data from the Moderate Resolution Imaging Spectroradiometer (MODIS), and meteorological reanalysis data. Our model performed well in estimating SIF (R2 = 0.79, root mean squared error (RMSE) = 0.07 W m−2 μm−1 sr−1). The model was then used to estimate SIF for each 0.05° × 0.05° grid cell and each 8-day interval for the study period. The resulting GOSIF product has reasonable seasonal cycles, and captures the similar seasonality as both the coarse-resolution OCO-2 SIF (1°), directly aggregated from the discrete OCO-2 soundings, and tower-based GPP. Our SIF estimates are highly correlated with GPP from 91 EC flux sites (R2 = 0.73, p &lt; 0.001). They capture the expected spatial and temporal patterns and also have remarkable ability to highlight the crop areas with the highest daily productivity across the globe. Our product also allows us to examine the long-term trends in SIF globally. Compared with the coarse-resolution SIF that was directly aggregated from OCO-2 soundings, GOSIF has finer spatial resolution, globally continuous coverage, and a much longer record. Our GOSIF product is valuable for assessing terrestrial photosynthesis and ecosystem function, and benchmarking terrestrial biosphere and Earth system models.

Improving Estimation of Gross Primary Production in Dryland Ecosystems by a Model-Data Fusion Approach

Accurate and continuous monitoring of the production of arid ecosystems is of great importance for global and regional carbon cycle estimation. However, the magnitude of carbon sequestration in arid regions and its contribution to the global carbon cycle is poorly understood due to the worldwide paucity of measurements of carbon exchange in arid ecosystems. The Moderate Resolution Imaging Spectroradiometer (MODIS) gross primary productivity (GPP) product provides worldwide high-frequency monitoring of terrestrial GPP. While there have been a large number of studies to validate the MODIS GPP product with ground-based measurements over a range of biome types. Few studies have comprehensively validated the performance of MODIS estimates in arid and semi-arid ecosystems, especially for the newly released Collection 6 GPP products, whose resolution have been improved from 1000 m to 500 m. Thus, this study examined the performance of MODIS-derived GPP by compared with eddy covariance (EC)-observed GPP at different timescales for the main ecosystems in arid and semi-arid regions of China. Meanwhile, we also improved the estimation of MODIS GPP by using in situ meteorological forcing data and optimization of biome-specific parameters with the Bayesian approach. Our results revealed that the current MOD17A2H GPP algorithm could, on the whole, capture the broad trends of GPP at eight-day time scales for the most investigated sites. However, GPP was underestimated in some ecosystems in the arid region, especially for the irrigated cropland and forest ecosystems (with R2 = 0.80, RMSE = 2.66 gC/m2/day and R2 = 0.53, RMSE = 2.12 gC/m2/day, respectively). At the eight-day time scale, the slope of the original MOD17A2H GPP relative to the EC-based GPP was only 0.49, which showed significant underestimation compared with tower-based GPP. However, after using in situ meteorological data to optimize the biome-based parameters of MODIS GPP algorithm, the model could explain 91% of the EC-observed GPP of the sites. Our study revealed that the current MODIS GPP model works well after improving the maximum light-use efficiency (εmax or LUEmax), as well as the temperature and water-constrained parameters of the main ecosystems in the arid region. Nevertheless, there are still large uncertainties surrounding GPP modelling in dryland ecosystems, especially for desert ecosystems. Further improvements in GPP simulation in dryland ecosystems are needed in future studies, for example, improvements of remote sensing products and the GPP estimation algorithm, implementation of data-driven methods, or physiology models.

Improving satellite-based modelling of gross primary production in deciduous broadleaf forests by accounting for seasonality in light use efficiency

ABSTRACTVegetation gross primary production (GPP), the photosynthetic yields by green plants per unit area per unit time, is a key metric of carbon flux in understanding the land–atmosphere interactions and terrestrial carbon cycles. Satellite-based light use efficiency (LUE) models are valuable methods to retrieve large-scale terrestrial GPP using remote sensing data. As studies have reported that maximum light use efficiency, a key parameter that is often assumed to be constant in the LUE models, there is a need to explore the effects of LUE seasonality on GPP simulation and ways for correction. This study proposes a method based on leaf area index to account for LUE seasonality and applies it to four different light use efficiency models (i.e., the MOD17 algorithm, the vegetation photosynthesis model, the radiation partitioning model, and the vegetation index model) for comparisons. Based on 59 site-years flux tower data from deciduous broadleaf forest sites in the United States, the results show that all models could simulate daily GPP time series well and explain more than 85.0% variance of tower-based GPP. There is, however, a tendency to overestimate GPP during the non-growing season but underestimate GPP during the growing season. By applying the correction function, GPP simulation using the LUE models improved in all experiments as indicated by increased correlation coefficients, the index of agreement and decreased root-mean-square errors. Among all models, the radiation partitioning model achieves the highest correlation coefficients between modelled and observed daily GPP likely because it considers the influences of direct and diffuse radiation partitioning on daily canopy photosynthesis. Our study indicates that satellite-based light use efficiency models could be successfully applied for deriving daily vegetation GPP and potentially producing daily routine satellite products, while considering the effects of LUE seasonality on canopy could help improve significantly the simulation accuracy of daily GPP in phenology.

Effects of Forest Canopy Vertical Stratification on the Estimation of Gross Primary Production by Remote Sensing

Gross primary production (GPP) in forests is the most important carbon flux in terrestrial ecosystems. Forest ecosystems with high leaf area index (LAI) values have diverse species or complex forest structures with vertical stratifications that influence the carbon–water–energy cycles. In this study, we used three light use efficiency (LUE) GPP models and site-level experiment data to analyze the effects of the vertical stratification of dense forest vegetation on the estimates of remotely sensed GPP during the growing season of two forest sites in East Asia: Dinghushan (DHS) and Tomakomai (TMK). The results showed that different controlling environmental factors of the vertical layers, such as temperature and vapor pressure deficit (VPD), produce different responses for the same LUE value in the different sub-ecosystems (defined as the tree, shrub, and grass layers), which influences the GPP estimation. Air temperature and VPD play important roles in the effects of vertical stratification on the GPP estimates in dense forests, which led to differences in GPP uncertainties from −50% to 30% because of the distinct temperature responses in TMK. The unequal vertical LAI distributions in the different sub-ecosystems led to GPP variations of 1–2 gC/m2/day with uncertainties of approximately −30% to 20% because sub-ecosystems have unique absorbed fractions of photosynthetically active radiation (APAR) and LUE. A comparison with the flux tower-based GPP data indicated that the GPP estimations from the LUE and APAR values from separate vertical layers exhibited better model performance than those calculated using the single-layer method, with 10% less bias in DHS and more than 70% less bias in TMK. The precision of the estimated GPP in regions with thick understory vegetation could be effectively improved by considering the vertical variations in environmental parameters and the LAI values of different sub-ecosystems as separate factors when calculating the GPP of different components. Our results provide useful insight that can be used to improve the accuracy of remote sensing GPP estimations by considering vertical stratification parameters along with the LAI of sub-ecosystems in dense forests.

Estimating Diurnal Courses of Gross Primary Production for Maize: A Comparison of Sun-Induced Chlorophyll Fluorescence, Light-Use Efficiency and Process-Based Models

Accurately quantifying gross primary production (GPP) is of vital importance to understanding the global carbon cycle. Light-use efficiency (LUE) models and process-based models have been widely used to estimate GPP at different spatial and temporal scales. However, large uncertainties remain in quantifying GPP, especially for croplands. Recently, remote measurements of solar-induced chlorophyll fluorescence (SIF) have provided a new perspective to assess actual levels of plant photosynthesis. In the presented study, we evaluated the performance of three approaches, including the LUE-based multi-source data synergized quantitative (MuSyQ) GPP algorithm, the process-based boreal ecosystem productivity simulator (BEPS) model, and the SIF-based statistical model, in estimating the diurnal courses of GPP at a maize site in Zhangye, China. A field campaign was conducted to acquire synchronous far-red SIF (SIF760) observations and flux tower-based GPP measurements. Our results showed that both SIF760 and GPP were linearly correlated with APAR, and the SIF760-GPP relationship was adequately characterized using a linear function. The evaluation of the modeled GPP against the GPP measured from the tower demonstrated that all three approaches provided reasonable estimates, with R2 values of 0.702, 0.867, and 0.667 and RMSE values of 0.247, 0.153, and 0.236 mg m−2 s−1 for the MuSyQ-GPP, BEPS and SIF models, respectively. This study indicated that the BEPS model simulated the GPP best due to its efficiency in describing the underlying physiological processes of sunlit and shaded leaves. The MuSyQ-GPP model was limited by its simplification of some critical ecological processes and its weakness in characterizing the contribution of shaded leaves. The SIF760-based model demonstrated a relatively limited accuracy but showed its potential in modeling GPP without dependency on climate inputs in short-term studies.