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 (GPP_EC). 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 GPP_EC 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 GPP_EC 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.

With the rapid growth of greenhouse gas (GHG) monitoring satellites, more and more studies focused on the issue of inversion/optimization of carbon dioxide (CO ₂ ) fluxes using satellite-derived XCO ₂ observations in recent years. A common and critical challenge in this framework is the separation of background and anomalies from XCO ₂ observations, which directly affect the performance of the CO ₂ fluxes’ inversion. We proposed a novel method to accurately extract background XCO ₂ from satellite observations. A series of observing system simulation experiments (OSSEs) were performed to test the performance of the method. We found that the bias and uncertainty of the background concentration are below 0.01 and 0.05 ppm in the given cases, respectively. Based on this method, we selected five overpasses from 2014 to 2016 to demonstrate a regional-scale flux inversion near Riyadh. The comparison with the two previous methods shows that the posterior simulated XCO ₂ by the method proposed in this article can match better with the observed XCO ₂ from Orbiting Carbon Observatory-2 (OCO-2).

Quantification of the distribution of the CO ₂ dry-air mixing ratio (XCO ₂ ) is crucial for understanding the carbon cycle. However, clouds and aerosols in the line of light create spectral interference with CO ₂ signals. This interference can result in a low yield of XCO ₂ retrievals, thus limiting the application of these valuable satellite data. In this study, we developed an innovative methodology to obtain XCO ₂ maps of high spatial and temporal resolution using satellite data. The method first interpolates the spatial properties using an empirical Bayesian kriging (EBK) algorithm. Then, the temporal properties are modulated based on a CO ₂ curve database that was constructed using temporal contours and transfer learning techniques. We applied this method to obtain spatiotemporal XCO ₂ maps over mainland China using the Orbiting Carbon Observatory 2 (OCO-2) data product OCO-2_L2_Lite_FP 9r for the period from January 1 to December 31, 2019. The correlation coefficient ( $R^{2}$ ) was 0.8056, and the average absolute prediction error [root-mean-square error (RMSE)] was 0.9951. In the research area of mainland China, the vacancy validation strategy was adopted and yielded $R^{2}$ and RMSE of 0.8230 and 0.9746, respectively. We used the 2018–2019 ground-based data from four Total Carbon Column Observing Network (TCCON) sites in Europe and 2016 Hefei sites in mainland China to evaluate the performance of this new mapping method, respectively. Also, we obtained $R^{2}$ of 0.8690 and the RMSE of 0.9056 in Europe and $R^{2}$ of 0.8473 and the RMSE of 0.7026 in mainland China, proving the robustness and high precision of our method. This mapping technique is capable of filling the spatiotemporal gaps of satellite measurements with the high accuracy and resolution needed for its scientific application; thus, it has the potential to augment the scientific returns of satellite missions (e.g., USA OCO-2 Japan GOSAT and Chinese TanSat).

The uncertainty of carbon fluxes of the terrestrial ecosystem is the highest among all flux components, calling for more accurate and efficient means to monitor land sinks. Gross primary productivity (GPP) is a key index to estimate the terrestrial ecosystem carbon flux, which describes the total amount of organic carbon fixed by green plants through photosynthesis. In recent years, the solar-induced chlorophyll fluorescence (SIF), which is a probe for vegetation photosynthesis and can quickly reflect the state of vegetation growth, emerges as a novel and promising proxy to estimate GPP. The launch of Orbiting Carbon Observatory 2 (OCO-2) further makes it possible to estimate GPP at a finer spatial resolution compared with Greenhouse Gases Observing Satellite (GOSAT), Global Ozone Monitoring Experiment-2 (GOME-2) and SCanning Imaging Absorption spectroMeter for Atmospheric CHartographY (SCIAMACHY). However, whether the relationship between GPP and SIF is linear or non-linear has always been controversial. In this research, we proposed a new model to estimate GPP using SIF and the atmospheric CO₂ concentration from OCO-2 as critical driven factors simultaneously (SIF-CO₂-GPP model). Evidences from all sites show that the introduction of the atmospheric CO₂ concentration improves accuracies of estimated GPP. Compared with the SIF-CO₂-GPP linear model, we found the SIF-GPP model overestimated GPP in summer and autumn but underestimated it in spring and winter. A series of simulation experiments based on SCOPE (Soil-Canopy Observation of Photosynthesis and Energy) was carried out to figure out the possible mechanism of improved estimates of GPP due to the introduction of atmospheric CO₂ concentrations. These experiments also demonstrate that there could be a non-linear relationship between SIF and GPP at half an hour timescale. Moreover, such relationships vary with CO₂ concentration. As OCO-2 is capable of providing SIF and XCO₂ products with identical spatial and temporal scales, the SIF-CO₂-GPP linear model would be implemented conveniently to monitor GPP using remotely sensed data. With the help of OCO-3 and its successors, the proposed SIF-CO₂-GPP linear model would play a significant role in monitoring GPP accurately in large geographical extents.

Carbon observation satellites based on passive theory (e.g., OCO-2/3, GOSAT-1/2, and TanSat) have relatively high carbon dioxide column concentration (XCO ₂ ) accuracy when the observation conditions are met. Passive satellites have data bias and coverage deficiencies due to cloud cover, low albedo, low light conditions, and aerosol scattering, resulting in carbon observation satellites based on passive theory that cannot meet the demand for high-precision, all-day, all-weather XCO ₂ monitoring. Active detection satellites are urgently needed to support global carbon sources, sinks, and carbon neutrality. China intends to launch a sensor satellite with active detection of XCO ₂ in the coming years. In this work, based on the satellite’s scaled-down airborne experiments, a spectral energy model was developed to optimize the conventional inversion algorithm and achieve a more accurate XCO ₂ inversion. The 1.572 μm IPDA lidar column length is used indirectly to evaluate the accuracy of the spectral energy model for signal extraction. And the experimental results show that the accuracy of the signal extracted by the 1.572 μm IPDA lidar column length is 0.74 and 6.20 m at sea and on land based on the indirect evaluation of the length of the 1.572 μm IPDA lidar column length. The optimized XCO ₂ was evaluated (standard deviation as an evaluation metric) and its XCO ₂ standard deviation reduced by 31%, 63% and 66% in the ocean, plains and mountains, respectively. Our algorithm can obtain the XCO ₂ with a consistent trend by using XCO ₂ from the OCO-2 satellite as a reference. The calculated XCO ₂ is more accurate in areas dominated by anthropogenic factors (plains), owing to the accuracy of the IPDA detection mechanism. This algorithm improves the accuracy and robustness of XCO ₂ inversion and has important reference significance for the IPDA lidar carried by China’s satellites to be launched in this year.

A novel framework was proposed to measure atmospheric concentration of δ13C using a multi-wavelength integrated path differential absorption (IPDA) LIDAR. The spectroscopy range of the multi-wavelength IPDA LIDAR is recommended as from 2264.5 to 2265.5 cm-1. Using the proposed retrieving method, the relative error of δ13C retrievals would be within 0.16 % under reasonable settings. Moreover, the proposed method shows reliable performances in different circumstances. It would be of great significance for exploring the characteristic of δ13C in ecosystem and anthropogenic emissions in future.