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.

Active remote sensing of atmospheric XCO₂ has several advantages over existing passive remote sensors, including global coverage, a smaller footprint, improved penetration of aerosols, and night observation capabilities. China is planning to launch a multi-functional atmospheric observation satellite equipped with a CO₂-IPDA (integrated path differential absorption Lidar) to measure columnar concentrations of atmospheric CO₂ globally. As space and power are limited on the satellite, compromises have been made to accommodate other passive sensors. In this study, we evaluated the sensitivity of the system’s retrieval accuracy and precision to some critical parameters to determine whether the current configuration is adequate to obtain the desired results and whether any further compromises are possible. We then mapped the distribution of random errors across China and surrounding regions using pseudo-observations to explore the performance of the planned CO₂-IPDA over these regions. We found that random errors of less than 0.3% can be expected for most regions of our study area, which will allow the provision of valuable data that will help researchers gain a deeper insight into carbon cycle processes and accurately estimate carbon uptake and emissions. However, in the areas where major anthropogenic carbon sources are located, and in coastal seas, random errors as high as 0.5% are predicted. This is predominantly due to the high concentrations of aerosols, which cause serious attenuation of returned signals. Novel retrieving methods must, therefore, be developed in the future to suppress interference from low surface reflectance and high aerosol loading.

The vertically distributed aerosol optical properties are investigated over Pakistan utilizing the Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation (CALIPSO) Level 2 products from 2007 to 2014. For a better understanding of the spatiotemporal characteristics of vertical aerosol layers, the interannual and seasonal variations of nine selected aerosol parameters such as the AOD of the lowest aerosol layer (AOD_L), the base height of the lowest aerosol layer (H_L), the top height of the highest aerosol layer (H_H), the volume depolarization ratio of the lowest aerosol layer (DR_L), the color ratio of the lowest aerosol layer (CR_L), total AOD of all the aerosol layers (AOD_T), the number of aerosol feature layers (N), the thickness of the lowest aerosol layer (T_L), the AOD proportion for the lowest aerosol layer (PAOD_L) for both day and night times are analyzed. The results show AOD_T increased slightly from 2007 to 2014 over Pakistan, and relatively high AOD_T exists over the Punjab and Sindh (southern region), which might be owing to the high level of economic development, frequent dust storms, and profound agricultural activities (anthropogenic emissions). AOD_T increases from north to south. The reason may be that the southern region is rapidly urbanized and is near the desert. The northern region is dominated by agricultural land, and cities are usually semi-urbanized. The highest AOD_T appears in summer compared to the other seasons, and during daytime compared to nighttime. The H_L and H_H vary significantly, owing to the topography of Pakistan. The N is relatively large over Punjab and Sindh compared to the other regions, which might be caused by relatively stronger atmospheric convections. The spatial distribution of the T_L showed an inverse relationship with the topography as lower values are observed over elevated regions such as Gilgit-Baltistan and Jammu-Kashmir. The value of the PAOD_L indicates that 77% of the total aerosols are mainly concentrated in the lowest layer of the atmosphere over Pakistan. The higher values of DR_L and CR_L indicate non-spherical and large particles over Balochistan and Sindh, which might be related to the proximity to the desert. This study provides very useful information about vertically distributed aerosol optical properties which could help researchers and policymakers to regulate and mitigate air pollution issues of Pakistan.

Aerosol scattering and absorption properties were continuously measured and analyzed at the urban Laboratory for Information Engineering in Surveying, Mapping and Remote Sensing (LIESMARS) site in Wuhan, central China, from 1 December 2009 to 31 March 2014. The mean aerosol scattering coefficient , absorption coefficient , and single scattering albedo (SSA) were 377.54 Mm⁻¹, 119.06 Mm⁻¹, and 0.73, respectively. Both and showed obvious annual variability with large values in winter and small values in summer, principally caused by the annual characteristics of meteorological conditions, especially planetary boundary layer height (PBLH) and local emissions. The SSA showed a slight annual variation. High values of SSA were related to formation of secondary aerosols in winter hazes and aerosol hygroscopic growth in humid summer. The large SSA in June can be attributed to the biomass combustion in Hubei and surrounding provinces. Both and showed double peak phenomena in diurnal variation resulting from the shallow stable PBLH at night and automobile exhaust emission during morning rush hours. The SSA also exhibited a double peak phenomenon related to the proportional variation of black carbon (BC) and light scattering particulates in the day and night. The long-term exploration on quantified aerosol optical properties can help offer scientific basis of introducing timely environmental policies for local government.

The purpose of this study is to investigate the air quality levels of Nanyang city according to Chinese air quality standards. Therefore, in this study, fine particulate matter (PM_2.5), coarse particulate matter (PM₁₀), and total suspended particulate (TSP) were analyzed from 19 November 2021 to 19 March 2022 in Nanyang city. The results show that the average concentrations of PM_2.5, PM_10, and TSP were 106.47 µg/m³, 137.32 µg/m³, and 283.40 µg/m³, respectively. The numbers of days that meet the national secondary air quality standard of 24-h average concentrations were 29.75% for PM_2.5, 63.64% for PM₁₀, and 63.64% for TSP, indicating that most of the time, the air quality of Nanyang city remains polluted in winter, especially with more contributions of PM_2.5 compared to PM₁₀ and TSP. The higher concentrations were observed between 07:00 and 08:00, suggesting that vehicular emissions can be a major cause of air pollution in Nanyang city. The results also show a significant positive correlation between particulate matter and relative humidity, and a weak correlation with temperature and wind speed, which suggests that higher relative humidity increases the formation of particulate matter. This study can provide theoretical support for the local government to formulate air pollution prevention and control policies for Nanyang city.

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.