Synthetic retrieval of hourly net ecosystem exchange using the neural network model with combined MI and GOCI geostationary sensor datasets and ground-based measurements

Net ecosystem carbon dioxide (CO₂) exchange (NEE) is a key parameter for understanding the terrestrial plant ecosystems, but it is difficult to monitor or predict over large areas at fine temporal resolutions. In this research, we estimated the hourly NEE using a combination of the integrated neural network (NN) model with geostationary satellite imagery to overcome the limitations of existing daily polar orbiting satellite-derived carbon flux products. Two sets of satellite imageries (i.e. the meteorological imager (MI) and geostationary ocean colour imager (GOCI) aboard communication, ocean, and meteorological satellite (COMS)) and CO₂ flux data derived from eddy covariance measurements were used to verify the feasibility of applying hourly geostationary satellite imagery with an NN-based approach for estimating NEE at high temporal resolutions. For the NN model, the optimum neuronal architecture was established using an NN with one hidden layer that was trained using the Levenberg–Marquardt back propagation algorithm. The hourly NEE values estimated in test period from the NN model using the combined COMS MI and GOCI imagery and ground measurements as model inputs were compared with the eddy covariance NEE values from the measurement tower, which yielded reliable statistical agreement. The hourly NEE results from the NN model based on COMS MI and GOCI imagery and ground measurement data had the highest accuracy (RMSE = 2.026 μmol m^?2 s^?2, R = 0.975), while the root mean square error (RMSE) and the regression coefficient (R) generated by the NN model based on satellite imagery as the sole input variable were relatively lower (RMSE = 3.230 μmol m^?2 s^?2, R = 0.952). Although the simulations for the satellite-only NEE were showed as lower accuracy than the NN model that included all input variables, the hourly variations in NEE also appeared to describe its daily growth and development pattern well, indicating the possibility of deriving hourly-based products from the proposed NN model using geostationary satellite data as inputs.     

Comparison of multiple models for remote sensing of carbon exchange using MODIS data in conifer-dominated forests

Boreal forests in the northern hemisphere provide important sinks for storing carbon dioxide (CO₂). However, the size and distribution of these sinks remain uncertain. In particular, many remote-sensing models show a strong bias in the simulation of carbon fluxes for evergreen needleleaf forest. The objective of this study is to improve these predictive models for accurately quantifying temporal changes in the net ecosystem exchange (NEE) of conifer-dominated forest solely based on satellite remote sensing, including the Moderate Resolution Imaging Spectroradiometer (MODIS) Terra daytime land-surface temperature (LST), night-time LST′, enhanced vegetation index (EVI), land–surface water index (LSWI), fraction of absorbed photosynthetically active radiation (FPAR), and leaf area index (LAI). Considering that the component fluxes, gross primary production (GPP), and ecosystem respiration (R_e), are strongly influenced by vegetation phenology, seasonality information was extracted from time-series MODIS EVI data based on non-linear least-squares fits of asymmetric Gaussian model functions with a software package for analysing the time-series of satellite sensor data (TIMESAT). The results indicated that models directly incorporating phenological information failed to improve their performance for temperate deciduous forest. Instead, three methods to retrieve the component fluxes – GPP and R_e – including direct estimates, models incorporating the phenological information, and models developed based on the threshold value (LST 273 K), were explored respectively. All methods improved NEE estimates markedly and models developed based on the threshold value performed best, and provided a future framework for accurate remote sensing of NEE in evergreen forest.     

Estimating northern peatland CO2 exchange from MODIS time series data

Studies using satellite sensor-derived data as input to models for CO₂ exchange show promising results for closed forest stands. There is a need for extending this approach to other land cover types, in order to carry out large-scale monitoring of CO₂ exchange. In this study, three years of eddy covariance data from two peatlands in Sweden were averaged for 16-day composite periods and related to data from the Moderate Resolution Imaging Spectroradiometer (MODIS) and modeled photosynthetic photon flux density (PPFD). Noise in the time series of MODIS 250 m vegetation indices was reduced by using double logistic curve fits. Smoothed normalized difference vegetation index (NDVI) showed saturation during summertime, and the enhanced vegetation index (EVI) generally gave better results in explaining gross primary productivity (GPP). The strong linear relationships found between GPP and the product of EVI and modeled PPFD (R² = 0.85 and 0.76) were only slightly stronger than for the product of EVI and MODIS daytime 1 km land surface temperature (LST) (R² = 0.84 and 0.71). One probable reason for these results is that several controls on GPP were related to both modeled PPFD and daytime LST. Since ecosystem respiration (ER) was largely explained by diurnal LST in exponential relationships (R² = 0.89 and 0.83), net ecosystem exchange (NEE) was directly related to diurnal LST in combination with the product of EVI and modeled PPFD in multiple exponential regressions (R² = 0.81 and 0.73). Even though the R² values were somewhat weaker for NEE, compared to GPP and ER, the RMSE values were much lower than if NEE would have been estimated as the sum of GPP and ER. The overall conclusion of this study is that regression models driven by satellite sensor-derived data and modeled PPFD can be used to estimate CO₂ fluxes in peatlands.     

Satellite-based modeling of gross primary production in an evergreen needleleaf forest

The eddy covariance technique provides valuable information on net ecosystem exchange (NEE) of CO₂, between the atmosphere and terrestrial ecosystems, ecosystem respiration, and gross primary production (GPP) at a variety of CO₂ eddy flux tower sites. In this paper, we develop a new, satellite-based Vegetation Photosynthesis Model (VPM) to estimate the seasonal dynamics and interannual variation of GPP of evergreen needleleaf forests. The VPM model uses two improved vegetation indices (Enhanced Vegetation Index (EVI), Land Surface Water Index (LSWI)). We used multi-year (1998-2001) images from the VEGETATION sensor onboard the SPOT-4 satellite and CO₂ flux data from a CO₂ eddy flux tower site in Howland, Maine, USA. The seasonal dynamics of GPP predicted by the VPM model agreed well with observed GPP in 1998-2001 at the Howland Forest. These results demonstrate the potential of the satellite-driven VPM model for scaling-up GPP of forests at the CO₂ flux tower sites, a key component for the study of the carbon cycle at regional and global scales.     

Modeling gross primary production of temperate deciduous broadleaf forest using satellite images and climate data

Net ecosystem exchange (NEE) of CO₂ between the atmosphere and forest ecosystems is determined by gross primary production (GPP) of vegetation and ecosystem respiration. CO₂ flux measurements at individual CO₂ eddy flux sites provide valuable information on the seasonal dynamics of GPP. In this paper, we developed and validated the satellite-based Vegetation Photosynthesis Model (VPM), using site-specific CO₂ flux and climate data from a temperate deciduous broadleaf forest at Harvard Forest, Massachusetts, USA. The VPM model is built upon the conceptual partitioning of photosynthetically active vegetation and non-photosynthetic vegetation (NPV) within the leaf and canopy. It estimates GPP, using satellite-derived Enhanced Vegetation Index (EVI), Land Surface Water Index (LSWI), air temperature and photosynthetically active radiation (PAR). Multi-year (1998-2001) data analyses have shown that EVI had a stronger linear relationship with GPP than did the Normalized Difference Vegetation Index (NDVI). Two simulations of the VPM model were conducted, using vegetation indices from the VEGETATION (VGT) sensor onboard the SPOT-4 satellite and the Moderate Resolution Imaging Spectroradiometer (MODIS) sensor onboard the Terra satellite. The predicted GPP values agreed reasonably well with observed GPP of the deciduous broadleaf forest at Harvard Forest, Massachusetts. This study highlighted the biophysical performance of improved vegetation indices in relation to GPP and demonstrated the potential of the VPM model for scaling-up of GPP of deciduous broadleaf forests.     

Relating NDVI to ecosystem CO2 exchange patterns in response to season length and soil warming manipulations in arctic Alaska

Climate change in the Arctic will differentially affect physiological rates, leaf phenology, and species composition of tundra, resulting in changing patterns and magnitudes of ecosystem CO₂ flux. The normalized difference vegetation index (NDVI) provides a potential means to infer changes in CO₂ flux, but whether relationships developed between NDVI and flux components can be generalized across the entire growing season and in response to changes induced by climate warming is uncertain. To investigate how well such changes might be assessed using multispectral digital images, ecosystem CO₂ fluxes and NDVI were compared throughout the 2002 growing season on experimental plots with increased growing season length and soil temperature at Toolik Lake, Alaska. Season length was increased by snow removal early in the season and soil temperatures were increased using heating cables. Carbon dioxide fluxes were measured using static chamber techniques and corresponding NDVI images were taken with an agricultural digital camera. The seasonal patterns of NDVI in all treatments showed an increase to a peak in early August followed by an abrupt decline, with the snow removal plots phenologically advanced compared to the controls. Net ecosystem production (NEP) showed uptake of CO₂ early in the season leveling out to a slight loss of CO₂ at peak season for both control and extended season plots. Gross primary productivity (GPP) closely followed the pattern of NDVI and the pattern of ecosystem respiration (R_e) mirrored that of GPP. NDVI was significantly correlated to GPP and ecosystem respiration (R² = 0.50 and 0.36 respectively) across plots, dates, and treatments combined. However, most of the covariation was across dates. After accounting for seasonal variation, NDVI never accounted for more than 25% of the remaining variation in flux measures. Analysis of covariance showed that a given NDVI value corresponded to different flux rates on different dates and to different R_e among treatments after correcting for date. The slopes of the NDVI-GPP and NDVI-Re relationships were much steeper across dates than across plots. These plot-scale results suggest that NDVI alone is not sufficient to estimate carbon flux rate responses to climate change across space or years.     

Dynamic change of CO2 flux over bare soil field and its relationship with remotely sensed surface temperature

This study investigated the potential of thermal remote sensing for estimating ecosystem surface CO₂ flux. Ecosystem surface CO₂ flux was measured by an eddy covariance method for more than 3 years, in conjunction with thermal and optical remote sensing measurements as well as micrometeorological, soil and plant measurements. The soil was Andisol (Hydric Hapludands), a humic volcanic ash soil, which is the major cultivated soil for upland crops in Japan. The soil surface CO₂ flux under bare soil conditions was best correlated with the remotely sensed surface temperature, while air temperature was less well correlated and soil temperature and soil water content were poorly correlated. The relationship was well expressed by an exponential Q ₁₀ function (r ²=0.66, RMSE=0.098). The value of Q ₁₀ and the threshold temperature at which the CO₂ flux approached zero were estimated to be 1.47 and 10.0°C, respectively. Results suggested that the soil surface temperature had the dominant effect on the microbial respiration as well as on the physical processes determining the CO₂ gas transfer at the soil–atmosphere interface. Remotely sensed surface temperature will provide useful information for investigation of CO₂ transfer processes near the soil surface, as well as for quantitative assessment of ecosystem surface CO₂ flux.     

Downscaling of passive microwave soil moisture retrievals based on spectral analysis

The retrieval of soil moisture from passive microwave remote-sensing data is presently one of the most effective methods for monitoring soil moisture. However, the spatial resolution of passive microwave soil moisture products is generally low; thus, existing soil moisture products should be downscaled in order to obtain more accurate soil moisture data. In this study, we explore the theoretical feasibility of applying the spectral downscaling method to the soil moisture in order to generate high spatial resolution soil moisture based on both Moderate Resolution Imaging Spectroradiometer and Fengyun-3B (FY3B) data. We analyse the spectral characteristics of soil moisture images covering the east-central of the Tibetan Plateau which have different spatial resolutions. The spectral analysis reveals that the spectral downscaling method is reliable in theory for downscaling soil moisture. So, we developed one spectral downscaling method for deriving the high spatial resolution (1 km) soil moister data from the FY3B data (25 km). Our results were compared with the ground truth measurements from 15 selected experimental days in 16 different sites. The average coefficient of determination (R²) of the spectral downscaling increased nearly doubled than that of the original FY3B soil moisture product. The spectral downscaled soil moister data were successfully applied to examine the water exchange between the land and atmosphere in the study regions. The spectral downscaling approach could be an efficient and effective method to improve the spatial resolution of current microwave soil moisture images.     

A thermal-based remote sensing technique for routine mapping of land-surface carbon,water and energy fluxes from field to regional scales

Robust yet simple remote sensing methodologies for mapping instantaneous land-surface fluxes of water, energy and CO₂ exchange within a coupled framework add significant value to large-scale monitoring networks like FLUXNET, facilitating upscaling of tower flux observations to address questions of regional carbon cycling and water availability. This study investigates the implementation of an analytical, light-use efficiency (LUE) based model of canopy resistance within a Two-Source Energy Balance (TSEB) scheme driven primarily by thermal remote sensing inputs. The LUE model computes coupled canopy-scale carbon assimilation and transpiration fluxes, and replaces a Priestley–Taylor (PT) based transpiration estimate used in the original form of the TSEB model. In turn, the thermal remote sensing data provide valuable diagnostic information about the sub-surface moisture status, obviating the need for precipitation input data and prognostic modeling of the soil water balance. Both the LUE and PT forms of the model are compared with eddy covariance tower measurements acquired in rangeland near El Reno, OK. The LUE method resulted in improved partitioning of the surface energy budget, capturing effects of midday stomatal closure in response to increased vapor pressure deficit and reducing errors in half-hourly flux predictions from 16 to 12%. The spatial distribution of CO₂ flux was mapped over the El Reno study area using data from an airborne thermal imaging system and compared to fluxes measured by an aircraft flying a transect over rangeland, riparian areas, and harvested winter wheat. Soil respiration contributions to the net carbon flux were modeled spatially using remotely sensed estimates of soil surface temperature, soil moisture, and leaf area index. Modeled carbon and water fluxes from this heterogeneous landscape compared well in magnitude and spatial pattern to the aircraft fluxes. The thermal inputs proved to be valuable in modifying the effective LUE from a nominal species-dependent value. The model associates cooler canopy temperatures with enhanced transpiration, indicating higher canopy conductance and carbon assimilation rates. The surface energy balance constraint in this modeling approach provides a useful and physically intuitive mechanism for incorporating subtle signatures of soil moisture deficiencies and reduced stomatal aperture, manifest in the thermal band signal, into the coupled carbon and water flux estimates.     

Evaluation of temporal variations in soil moisture based on the microwave polarization difference index using in situ data over agricultural areas in China

Soil moisture plays a critical role in the energy exchange and water redistribution of the land-atmosphere system. Knowledge of the temporal variations in soil moisture is vital in agricultural applications. Microwave indices are often used to characterize the temporal variations in soil moisture. In this study, we evaluate the temporal variations in soil moisture based on the microwave polarization difference index (MPDI) using ground-based measurements in China. In situ soil moisture at six test sites during the crop-growing season from 2009 to 2011 is obtained. The consistency of the temporal variations between the MPDI values and the in situ soil moisture is analysed in terms of (1) microwave frequencies, (2) satellite overpass times, and (3) measurement depths of soil moisture. The results show that the accuracies of the consistency vary from approximately 40% to 90%. Compared with the in situ soil moisture at 0–10 cm, the temporal variations in soil moisture are best characterized by the 6.9 GHz MPDI values from the ascending overpasses (MPDI_06A). Furthermore, the accuracies of the consistency between MPDI_06A and the in situ soil moisture at 0–10 cm are greater than those between MPDI_06A and the in situ soil moisture at 10–20 cm.     

Comparing the utility of microwave and thermal remote-sensing constraints in two-source energy balance modeling over an agricultural landscape

A two-source (soil + vegetation) energy balance model using microwave-derived near-surface soil moisture as a key boundary condition (TSM_SM) and another scheme using thermal-infrared (radiometric) surface temperature (TSM_TH) were applied to remote sensing data collected over a corn and soybean production region in central Iowa during the Soil Moisture Atmosphere Coupling Experiment (SMACEX)/Soil Moisture Experiment of 2002 (SMEX02). The TSM_SM was run using fields of near-surface soil moisture from microwave imagery collected by aircraft on six days during the experiment, yielding a root mean square difference (RMSD) between model estimates and tower measurements of net radiation (Rn) and soil heat flux (G) of approximately 20 W m^− 2, and 45 W m^− 2 for sensible (H) and latent heating (LE). Similar results for H and LE were obtained at landscape/regional scales when comparing model output with transect-average aircraft flux measurements. Flux predictions from the TSM_SM and TSM_TH models were compared for two days when both airborne microwave-derived soil moisture and radiometric surface temperature (T_R) data from Landsat were available. These two days represented contrasting conditions of moderate crop cover/dry soil surface and dense crop cover/moist soil surface. Surface temperature diagnosed by the TSM_SM was also compared directly to the remotely sensed T_R fields as an additional means of model validation. The TSM_SM performed well under moderate crop cover/dry soil surface conditions, but yielded larger discrepancies with observed heat fluxes and T_R under the high crop cover/moist soil surface conditions. Flux predictions from the thermal-based two-source model typically showed biases of opposite sign, suggesting that an average of the flux output from both modeling schemes may improve overall accuracy in flux predictions, in effect incorporating multiple remote-sensing constraints on canopy and soil fluxes.     

Calibration of remotely sensed, coarse resolution NDVI to CO2 fluxes in a sagebrush-steppe ecosystem

The net ecosystem exchange (NEE) of carbon flux can be partitioned into gross primary productivity (GPP) and respiration (R). The contribution of remote sensing and modeling holds the potential to predict these components and map them spatially and temporally. This has obvious utility to quantify carbon sink and source relationships and to identify improved land management strategies for optimizing carbon sequestration. The objective of our study was to evaluate prediction of 14-day average daytime CO₂ fluxes (F_day) and nighttime CO₂ fluxes (R_n) using remote sensing and other data. F_day and R_n were measured with a Bowen ratio-energy balance (BREB) technique in a sagebrush (Artemisia spp.)-steppe ecosystem in northeast Idaho, USA, during 1996-1999. Micrometeorological variables aggregated across 14-day periods and time-integrated Advanced Very High Resolution Radiometer (AVHRR) Normalized Difference Vegetation Index (iNDVI) were determined during four growing seasons (1996-1999) and used to predict F_day and R_n. We found that iNDVI was a strong predictor of F_day (R²=0.79, n=66, P<0.0001). Inclusion of evapotranspiration in the predictive equation led to improved predictions of F_day (R²=0.82, n=66, P<0.0001). Crossvalidation indicated that regression tree predictions of F_day were prone to overfitting and that linear regression models were more robust. Multiple regression and regression tree models predicted R_n quite well (R²=0.75-0.77, n=66) with the regression tree model being slightly more robust in crossvalidation. Temporal mapping of F_day and R_n is possible with these techniques and would allow the assessment of NEE in sagebrush-steppe ecosystems. Simulations of periodic F_day measurements, as might be provided by a mobile flux tower, indicated that such measurements could be used in combination with iNDVI to accurately predict F_day. These periodic measurements could maximize the utility of expensive flux towers for evaluating various carbon management strategies, carbon certification, and validation and calibration of carbon flux models.     

Modeling gross primary production of alpine ecosystems in the Tibetan Plateau using MODIS images and climate data

The eddy covariance technique provides measurements of net ecosystem exchange (NEE) of CO₂ between the atmosphere and terrestrial ecosystems, which is widely used to estimate ecosystem respiration and gross primary production (GPP) at a number of CO₂ eddy flux tower sites. In this paper, canopy-level maximum light use efficiency, a key parameter in the satellite-based Vegetation Photosynthesis Model (VPM), was estimated by using the observed CO₂ flux data and photosynthetically active radiation (PAR) data from eddy flux tower sites in an alpine swamp ecosystem, an alpine shrub ecosystem and an alpine meadow ecosystem in Qinghai-Tibetan Plateau, China. The VPM model uses two improved vegetation indices (Enhanced Vegetation Index (EVI), Land Surface Water Index (LSWI)) derived from the Moderate Resolution Imaging Spectral radiometer (MODIS) data and climate data at the flux tower sites, and estimated the seasonal dynamics of GPP of the three alpine grassland ecosystems in Qinghai-Tibetan Plateau. The seasonal dynamics of GPP predicted by the VPM model agreed well with estimated GPP from eddy flux towers. These results demonstrated the potential of the satellite-driven VPM model for scaling-up GPP of alpine grassland ecosystems, a key component for the study of the carbon cycle at regional and global scales.     

Evaluation of soil moisture from satellite observations over South America

A study was performed to evaluate the surface soil moisture derived from Advanced Microwave Scanning Radiometer for the Earth Observing System (AMSR-E) sensor observations over South America. Other soil moisture and rainfall datasets were also used for the analysis. The information for the soil data came from the Eta regional climate model, and for the rainfall data from the Tropical Rainfall Microwave Mission (TRMM) satellite. Statistical analysis was used to evaluate the quality of the soil moisture and rainfall products, with estimates of the correlation coefficient (R), χ² and Cramer's phi (?_c). The results show high correlations (R > 0.8) of the AMSR-E soil moisture products with the Eta model for different regions of South America. Comparison of soil moisture products with rainfall datasets showed that the AMSR-E C-band soil moisture product was highly correlated with the TRMM satellite rainfall datasets, with the highest values of χ² and ?. The results show that the AMSR-E C-band soil moisture products contain important information that can be used for various purposes, such as monitoring floods or droughts in arid areas or as input within the framework of an assimilation scheme of numerical weather prediction models.     

Seasonal and inter-annual variation in surface energy fluxes and forcing parameters in agro-climatic regions of India

Understanding changes in monsoon variability over a decade requires thorough knowledge of the seasonal and inter-annual variability in surface energy flux and its forcing parameters (land surface and meteorology) in response to climate change. In the present study, the Moderate Resolution Imaging Spectroradiometer (MODIS) Aqua climate model gridded global products (0.05° × 0.05° spatial resolution) of land surface temperature (LST; T_s), normalized difference vegetation index (NDVI), and surface albedo (α) were used to generate seasonal (June–September) and inter-annual (2003–2012) variation in surface energy flux and its forcing parameters over different agro-climatic regions (ACRs) of India. Energy fluxes were retrieved using a single-source surface energy balance model (here vegetation and soil is considered as a single unit). Energy flux observations over different ACRs allowed comparison of the seasonal transition of latent heat flux (LE), net radiation (R_n), soil heat flux (G), available energy (Q = R_n – G), and evaporative fraction (EF) as terrestrial links to the atmosphere. The seasonal and inter-annual variation in EF was investigated by plotting against the soil moisture information retrieved from the Advanced Microwave Scanning Radiometer-EOS (AMSR-E) global monthly data product (1° × 1° spatial resolution). Decadal and seasonal analysis showed that energy fluxes vary widely in time and space due to variability in surface radiation parameters (T_s, α), vegetation cover, soil moisture, and air temperature (T_a), which influence the seasonal transition of monsoon through LE and EF. Among the ACRs, LE and EF were found lowest in the Western Dry Region (WDR) and highest in the Western Himalayan Region (WHR). The spatiotemporal depiction of MODIS LE and MODIS EF over a span of 10 years can identify the hotspots and monsoon intensity over different ACRs. Climatic parameters that are susceptible to changes resulting from climate change are thoroughly studied in the present analysis.     

Spectral indices for remote sensing of phytomass,deciduous shrubs,and productivity in Alaskan Arctic tundra

The relationships among in situ spectral indices, phytomass, plant functional types, and productivity were determined through field observations of moist acidic tundra (MAT), moist non-acidic tundra (MNT), heath tundra (HT), and sedge-shrub tundra (SST) in the Arctic coastal tundra, Alaska, USA. The two-band enhanced vegetation index (EVI2) was found more useful for estimating vascular plant green phytomass, leaf carbon and nitrogen, leaf carbon and nitrogen turnover, and vascular plant net primary productivity (NPP) without root production than the normalized difference vegetation index (NDVI). Deciduous shrub green phytomass was strongly correlated with deciduous shrub index (DSI), defined as EVI2 × (R_blue + R_green – R_red)/(R_blue + R_green + R_red) (with a coefficient of determination (R²) of 0.63). R_blue, R_green, and R_red denote the blue, green, and red bands, respectively. This is because R_blue and R_green values were higher than the R_red values for green leaves, deciduous shrub stems, lichens, and rocks compared with other ecosystem components, and EVI2 values of lichens and rocks were very low. The vascular plant NPP without root production was estimated with an R² of 0.67 using DSI and EVI2. Our results offer empirical evidence that a new spectral index predicts the distribution of deciduous shrub and plant production, which influences the interactions between tundra ecosystems and the atmosphere.     

Comparison analysis of the global carbon dioxide concentration column derived from SCIAMACHY,AIRS, and GOSAT with surface station measurements

As a significant greenhouse gas, CO₂ plays an important role in not only the formation of, but also in changes to, the Earth’s climate. A series of remote-sensing detectors have been launched into space to aid the understanding of sources and sinks of CO₂. Although measurements from AIRS (Atmospheric Infrared Sounder), SCIAMACHY (Scanning Imaging Absorption Spectrometer for Atmospheric Cartography), and GOSAT (Greenhouse Gases Observing Satellite) have been frequently used to retrieve atmospheric CO₂ concentrations, there are no comprehensive comparison analyses between satellite data and ground data. In this article, the characteristics of the current common observing platforms and their product data are compared and analysed. Correlation coefficient, RMSD, and bias are used to evaluate the CO₂ retrieved from satellite data. The results reveal that: SCIAMACHY has limitations in detecting CO₂ over the ocean; GOSAT has the poorest coverage on a global scale but has a better capability to detect CO₂ over the sea than SCIAMACHY; and AIRS can reflect the distribution and changes of CO₂ very well. The differences in coverage and accuracy indicate the necessity to produce consistent products with improved spatial and temporal features and indicate a future development trend for instruments including higher spectral resolution, higher spatial resolution, wider coverage, shorter revisit periods, and higher signal to noise ratios.     

Remote-sensing observations relevant to ocean acidification

Ocean acidification, a consequence of the ocean absorbing about a third of the anthropogenic carbon dioxide (CO₂) emitted into the atmosphere, is poised to affect biogeochemical cycles and the seawater chemical system. Traditional research methods, such as field and in situ sampling, are precise and reliable, but are inherently limited in spatial and temporal coverage and resolution. This article summarizes remotely sensed products, including air-sea CO₂ fluxes, total alkalinity, suspended calcite (particulate inorganic carbon), particulate organic carbon and calcification rates, which can be used to observe ocean acidification indirectly. Confounding factors and limitations of algorithms are major sources of errors. This article also discusses remote-sensing algorithms and satellite technology developments. Remote sensing, considering its great advantages and successful applications in climate change, will be an important tool in future studies of ocean acidification.     

Continental-scale net radiation and evapotranspiration estimated using MODIS satellite observations

Evapotranspiration (ET) is a major pathway for water loss from many ecosystems, and its seasonal variation affects soil moisture and net ecosystem CO₂ exchange. We developed an algorithm to estimate ET using a semi-empirical Priestley-Taylor (PT) approach, which can be applied at a range of spatial scales. We estimated regional net radiation (R_net) at monthly time scales using MODerate resolution Imaging Spectroradiometer (MODIS) albedo and land surface temperature. Good agreement was found between satellite-based estimates of monthly R_net and field-measured R_net, with a RMSE of less than 30 W m^− 2. An adjustable PT coefficient was parameterized as a function of leaf area index and soil moisture based on observations from 27 AmeriFlux eddy covariance sites. The biome specific optimization using tower-based observations performed well, with a RMSE of 17 W m^− 2 and a correlation of 0.90 for predicted monthly latent heat. We implemented the approach within the hydrology module of the CASA biogeochemical model, and used it to estimate ET at a 1 km spatial resolution for the conterminous United States (CONUS). The RMSE of modeled ET was reduced to 21.1 mm mon^− 1, compared to 27.1 mm mon^− 1 in the original CASA model. The monthly ET rates averaged over the Mississippi River basin were similar to those derived using GRACE satellite measurements and river discharge data. ET varied substantially over the CONUS, with annual mean values of 110 ± 76 mm yr^− 1 in deserts, 391 ± 176 mm yr^− 1 in savannas and grasslands, and 840 ± 234 mm yr^− 1 in broadleaf forests. The PT coefficient was the main driver for the spatial variation of ET in arid areas, whereas R_net controlled ET when mean annual precipitation was higher than approximately 400 mm yr^− 1.     

Improving estimates of fractional vegetation cover based on UAV in alpine grassland on the Qinghai–Tibetan Plateau

Fractional vegetation cover (FVC) is an important parameter in studies of ecosystem balance, soil erosion, and climate change. Remote-sensing inversion is a common approach to estimating FVC. However, there is an important gap between ground-based surveys (quadrat level) and remote-sensing imagery (satellite image pixel scale) from satellites. In this study we evaluated that gap with unmanned aerial vehicle (UAV) aerial images of alpine grassland on the Qinghai–Tibetan Plateau (QTP). The results showed that: (1) the most accurate estimations of FVC came from UAV (FVC_UAV) at the satellite image pixel scale, and when FVC was estimated using ground-based surveys (FVC_ground), the accuracy increased as the number of quadrats used increased and was inversely proportional to the heterogeneity of the underlying surface condition; (2) the UAV method was more efficient than conventional ground-based survey methods at the satellite image pixel scale; and (3) the coefficient of determination (R²) between FVC_UAV and vegetation indices (VIs) was significantly greater than that between FVC_ground and VIs (p < 0.05, n = 5). Our results suggest that the use of UAV to estimate FVC at the satellite image pixel scale provides more accurate results and is more efficient than conventional ground-based survey methods.