Satellite-based modeling of gross primary production in a seasonally moist tropical evergreen forest

A CO₂ eddy flux tower study has recently reported that an old-growth stand of seasonally moist tropical evergreen forest in Santarém, Brazil, maintained high gross primary production (GPP) during the dry seasons [Saleska, S. R., Miller, S. D., Matross, D. M., Goulden, M. L., Wofsy, S. C., da Rocha, H. R., de Camargo, P. B., Crill, P., Daube, B. C., de Freitas, H. C., Hutyra, L., Keller, M., Kirchhoff, V., Menton, M., Munger, J. W., Pyle, E. H., Rice, A. H., & Silva, H. (2003). Carbon in amazon forests: Unexpected seasonal fluxes and disturbance-induced losses. Science, 302, 1554-1557]. It was proposed that seasonally moist tropical evergreen forests have evolved two adaptive mechanisms in an environment with strong seasonal variations of light and water: deep roots system for access to water in deep soils and leaf phenology for access to light. Identifying tropical forests with these adaptive mechanisms could substantially improve our capacity of modeling the seasonal dynamics of carbon and water fluxes in the tropical zone. In this paper, we have analyzed multi-year satellite images from the VEGETATION (VGT) sensor onboard the SPOT-4 satellite (4/1998-12/2002) and the Moderate Resolution Imaging Spectroradiometer (MODIS) onboard the Terra satellite (2000-2003). We reported temporal analyses of vegetation indices and simulations of the satellite-based vegetation photosynthesis model (VPM). The Enhanced Vegetation Index (EVI) identified subtle changes in the seasonal dynamics of leaf phenology (leaf emergence, leaf aging and leaf fall) in the forest, as suggested by the leaf litterfall data. The land surface water index (LSWI) indicated that the forest experienced no water stress in the dry seasons of 1998-2002. The VPM model, which uses EVI, LSWI and site-specific climate data (air temperature and photosynthetically active radiation, PAR) for 2001-2002, predicted high GPP in the late dry seasons, consistent with observed high evapotranspiration and estimated GPP from the CO₂ eddy flux tower.     

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.     

Estimating daily gross primary production of maize based only on MODIS WDRVI and shortwave radiation data

Accurate assessment of temporal changes in gross primary production (GPP) is important for carbon budget assessments and evaluating the impact of climate change on crop productivity. The objective of this study was to devise a simple remote sensing-based GPP model to quantify daily GPP of maize. In the model, (1) daily shortwave radiation (SW), derived from the reanalysis data (North American Land Data Assimilation System; NLDAS-2) and (2) smoothed Wide Dynamic Range Vegetation Index (WDRVI) data, derived from Moderate Resolution Imaging Spectroradiometer (MODIS) 250-m observations were used as proxy variables of the incident photosynthetically active radiation (PAR) and the total canopy chlorophyll content, respectively. The model was calibrated and validated by using tower-based CO₂ flux observations over an 8-year period (2001 to 2008) for one rainfed and two irrigated sites planted to maize as part of the Carbon Sequestration Program at the University of Nebraska-Lincoln. The results showed the temporal features of the product SW*WDRVI closely related to the temporal GPP variations in terms of both daily variations and seasonal patterns. The simple GPP model was able to predict the daily GPP values and accumulated GPP values of maize with high accuracy.     

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.     

Remote sensing of tundra gross ecosystem productivity and light use efficiency under varying temperature and moisture conditions

Satellite observations have shown greening trends in tundra in response to climate change, suggesting increases in productivity. To better understand the ability of remote sensing to detect climate impacts on tundra vegetation productivity, we applied a photosynthetic light use efficiency model to simulated climate change treatments of tundra vegetation. We examined changes in the Normalized Difference Vegetation Index (NDVI) and photosynthetic light use efficiency (ε) in experimental warming and moisture treatments designed to simulate climate change in northern Alaska. Plots were warmed either passively, using Open Top Chambers, or actively using electric heaters in the soil. In one set of plots water table depth was actively altered, while other plots were established in locations that were naturally wet or dry. Over two growing seasons, plot-level carbon flux and spectral reflectance measurements were collected, and the results were used to derive a light use efficiency model that could explore the effects of moisture and temperature treatments using remote sensing.Warming increased values of canopy greenness (NDVI) relative to control plots, this effect being more pronounced in wet plots than in dry plots. Light use efficiency (LUE), the relationship between absorbed photosynthetically active radiation (PAR) and gross ecosystem production (GEP), was consistent across warming treatments, growing season, subsequent years, and sites. However, LUE was affected by vegetation type, which varied with moisture; plots in naturally dry locations showed reduced light use efficiency relative to moist plots. Additionally moss exhibited reduced LUE relative to vascular plants. Understory moss production, not accounted for by the usual definition of the fraction of absorbed PAR (f_APAR), was found to be a significant part of total GEP, particularly in areas with low vascular plant cover. These results support the use of light use efficiency models driven by spectral reflectance for estimating GEP in tundra vegetation, provided effects of vegetation functional type (e.g. mosses versus vascular plants) and microtopography are considered.     

Comparison of multiple models for estimating gross primary production using MODIS and eddy covariance data in Harvard Forest

Gross primary production (GPP) defined as the overall rate of fixation of carbon through the process of vegetation photosynthesis is important for carbon cycle and climate change research. Three models, the Vegetation Photosynthesis Model (VPM), the Temperature and Greenness (TG) model and the Vegetation Index (VI) model have been compared for the estimation of GPP in Harvard Forest from 2003 to 2006 using climate variables acquired by eddy covariance (EC) measurements and Moderate Resolution Imaging Spectroradiometer (MODIS) satellite images. All these models provide more reliable estimates of GPP than that of MODIS GPP product. High Pearsons correlation coefficients r equal to 0.94, 0.92 and 0.90 are observed for the VPM, the TG and the VI model, respectively. Relationships between GPP and land surface temperature (LST, R² = 0.72), and vapor pressure deficit (VPD, R² = 0.45) indicate that climate variables are important for GPP estimation. Due to proper characterization of temperature, water stress and leaf age by three scalars, VPM best follows the seasonal variations of GPP. By incorporation of the MODIS surface reflectance and LST product, the TG model is the most suitable choice for areas without prior knowledge as it is based entirely on remote sensing observations. Results from the VI model demonstrate the possibility of using a single vegetation index for light use efficiency (LUE) estimation in deciduous forest that is of high spatial heterogeneity. The validation and comparison of models will be helpful in development of future GPP models using combinations of climate variables and/or remote sensing observations.     

Deriving maximal light use efficiency from coordinated flux measurements and satellite data for regional gross primary production modeling

Remote sensing models based on light use efficiency (LUE) provide promising tools for monitoring spatial and temporal variation of gross primary production (GPP) at regional scale. In most of current LUE-based models, maximal LUE (ε_max) heavily relies on land cover types and is considered as a constant, rather than a variable for a certain vegetation type or even entire eco-region. However, species composition and plant functional types are often highly heterogeneous in a given land cover class; therefore, spatial heterogeneity of ε_max must be fully considered in GPP modeling, so that a single cover type does not equate to a single ε_max value. A spatial dataset of ε_max accurately represents the spatial heterogeneity of maximal light use would be of significant beneficial to regional GPP models. Here, we developed a spatial dataset of ε_max by integrating eddy covariance flux measurements from 14 field sites in a network of coordinated observation across northern China and satellite derived indices such as enhanced vegetation index (EVI) and visible albedo to simulate regional distribution of GPP. This dynamic modeling method recognizes the spatial heterogeneity of ε_max and reduces the uncertainties in mixed pixels. Further, we simulated GPP with the spatial dataset of ε_max generated above. Both ε_max and growing season GPP show complex patterns over northern China that reflect influences of humidity, green vegetation fractions, and land use intensity. “Green spots” such as oasis meadow and alpine forests in dryland and “brown spots” such as build-up and heavily degraded vegetation in the east are clearly captured by the simulation. The correlation between simulated GPP and EC measured GPP indicate that the simulated GPP from this new approach is well matched with flux-measured GPP. Those results have demonstrated the importance of considering ε_max as both a spatially and temporally variable values in GPP modeling.     

Remote estimation of gross primary production in maize and support for a new paradigm based on total crop chlorophyll content

The accurate quantification of gross primary production (GPP) in crops is important for regional and global studies of carbon budgets. Because of the observed close relationship between GPP and total canopy chlorophyll content in crops, vegetation indices related to chlorophyll can be used as a proxy of GPP. In this study, we justified the approach, tested the performance of several widely used chlorophyll-related vegetation indices in estimating total chlorophyll content and GPP in maize based on spectral data collected at a close range, 6 meters above the top of the canopy, over a period of eight years (2001 to 2008). The results show that GPP can be accurately estimated with chlorophyll-related indices that use near infra-red and either green or the red edge range of the spectrum. These indices provide the best approximation of the widely variable GPP in maize under both irrigated and rainfed conditions.     

Exploring the potential of MODIS EVI for modeling gross primary production across African ecosystems

One of the most frequently applied methods for integrating controls on primary production through satellite data is the light use efficiency (LUE) approach, which links vegetation gross or net primary productivity (GPP or NPP) to remotely sensed estimates of absorbed photosynthetically active radiation (APAR). Eddy covariance towers provide continuous measurements of carbon flux, presenting an opportunity for evaluation of satellite estimates of GPP. Here we investigate relationships between eddy covariance estimated GPP, environmental variables derived from flux towers, Moderate Resolution Imaging Spectroradiometer (MODIS) enhanced vegetation index (EVI) and GPP across African savanna ecosystems. MODIS GPP was found to underestimate GPP at the majority of sites, particularly at sites in the Sahel. EVI was found to correlate well with estimated GPP on a site-by-site basis. Combining EVI with tower-measured PAR and evaporative fraction (EF, a measure of water sufficiency) improved the direct relationship between GPP and EVI at the majority of the sites. The slope of this relationship was strongly related to site peak leaf area index (LAI). These results are promising for the extension of GPP through the use of remote sensing data to a regional or even continental scale.     

Development of SAW-based multi-gas sensor for simultaneous detection of CO2 and NO2

A 440 MHz wireless and passive surface acoustic wave (SAW)-based multi-gas sensor integrated with a temperature sensor was developed on a 41° YX LiNbO₃ piezoelectric substrate for the simultaneous detection of CO₂, NO₂, and temperature. The developed sensor was composed of a SAW reflective delay lines structured by an interdigital transducer (IDT), ten reflectors, a CO₂ sensitive film (Teflon AF 2400), and a NO₂ sensitive film (indium tin oxide). Teflon AF 2400 was used for the CO₂ sensitive film because it provides a high CO₂ solubility, with good permeability and selectivity. For the NO₂ sensitive film, indium tin oxide (ITO) was used. Coupling of mode (COM) modeling was conducted to determine the optimal device parameters prior to fabrication. Using the parameters determined by the simulation results, the device was fabricated and then wirelessly measured using a network analyzer. The measured reflective coefficient S₁₁ in the time domain showed high signal/noise (S/N) ratio, small signal attenuation, and few spurious peaks. The time positions of the reflection peaks were well matched with the predicted values from the simulation. High sensitivity and selectivity were observed at each target gas testing. The obtained sensitivity was 2.12°/ppm for CO₂ and 51.5°/ppm for NO₂, respectively. With the integrated temperature sensor, temperature compensation was also performed during gas sensitivity evaluation process.     

Comparison of MODIS, eddy covariance determined and physiologically modelled gross primary production (GPP) in a Douglas-fir forest stand

Quantification of the magnitude of net terrestrial carbon (C) uptake, and how it varies inter-annually, is an important question with future potential sequestration influenced by both increased atmospheric CO₂ and changing climate. However the assessment of differences in measured and modeled C accumulation is a challenging task due to the significant fine scale variation occurring in terrestrial productivity due to soil, climate and vegetation characteristics as well as difficulties in measuring carbon accumulation over large spatial areas. The Moderate Resolution Imaging Spectroradiometer (MODIS) offers a means of monitoring gross primary production (GPP), both spatially and temporally, routinely from space. However it is critical to compare and contrast the temporal dynamics of the C and water fluxes with those measured from ground-based networks, or estimated using physiological models. In this paper, using a number of approaches, our objective is to determine if any systematic biases exists in either the MODIS, or the modeled estimates of fluxes, relative to the measurements made over an evergreen, needleleaf temperate rainforest on Vancouver Island, Canada. Results indicate that 8-day GPP as predicted with a simple physiological model (3PGS), forced using local meteorology and canopy characteristics, matched measured fluxes very well (r² = 0.86, p < 0.001) with no significant difference between eddy covariance (EC) and modeled GPP (p < 0.001). In addition, modeled water supply closely matched measured relative available soil water content at the site. Using canopy characteristics from the MODIS fraction of photosynthetically active radiation (fPAR) algorithm, slightly reduced the correspondence of the predictions due to a large number of unsuccessful retrievals (83%) due to sun angle, snow and cloud. Predictions of GPP based on the MODIS GPP algorithm, forced using local meteorology and canopy characteristics, were also highly correlated with EC measurements (r² = 0.89, p < 0.001) however these estimates were biased under predicting GPP. Estimates of GPP based on the most recent MODIS reprocessing (collection 4.5) remained highly correlated (r² = 0.88, p < 0.001) yet were also the most biased with the estimates being 30% less than the EC-measured GPP. Most of the variance in GPP at the site was explained by the absorbed photosynthetically active radiation. We also compared the nighttime respiration as measured over 2 years at the site with the minimum 8-day MODIS land surface temperature and found a significant relationship (r² = 0.57), similar to other studies.     

Designing cyclic pressure pulsing in naturally fractured reservoirs using an inverse looking recurrent neural network

In this paper, an inverse looking approach is presented to efficiently design cyclic pressure pulsing (huff ‘n’ puff) with N₂ and CO₂, which is an effective improved oil recovery method in naturally fractured reservoirs. A numerical flow simulation model with compositional, dual-porosity formulation is constructed. The model characteristics are from the Big Andy Field, which is a depleted, naturally fractured oil reservoir in Kentucky. A set of cyclic pulsing design scenarios is created and run using this model. These scenarios and corresponding performance indicators are fed into the recurrent neural network for training. In order to capture the cyclic, time-dependent behavior of the process, recurrent neural networks are used to develop proxy models that can mimic the reservoir simulation model in an inverse looking manner. Two separate inverse looking proxy models for N₂ and CO₂ injections are constructed to predict the corresponding design scenarios, given a set of desired performance characteristics. Predictive capabilities of developed proxy models are evaluated by comparing simulation outputs with neural-network outputs. It is observed that networks are able to accurately predict the design parameters, such as the injection rate and the duration of injection, soaking and production periods.     

Separate assessment of chemoresistivity and Schottky-type gas sensitivity in M–metal oxide–M′ structures

Maximum response levels reported for chemoresistive sensors span from less than 10 to over 10⁸. These differences are attributed to either the different micro- and nano-structured oxide pallets used or the properties of the metal–metal oxide junctions provided. Here, we report separate measurements and model-based estimations of the chemical responses arising from these different origins. The results quantitatively connect the observed responses to the parameters of the metal and metal oxide components of the device. It is shown that while the peak chemoresistive response is microstructure-dependent, the highest attainable Schottky-type gas sensitivity is almost microstructure-independent and is determined by the intrinsic properties of the materials involved. Measurements carried out on different Ag–TiO₂–Ti, Au–TiO₂–Ti and Ti–TiO₂–Ti structures verified the estimations: While chemoresistive responses in TiO₂ can hardly rise over 10², atmosphere-sensitive noble metal–TiO₂ junctions can cause responses as high as ∼10⁷.     

Carbon dioxide sensing properties of bismuth cobaltite

Bismuth cobaltite with sillenite-type structure was prepared from Co(OH)₂ and Bi(NO₃)₃·6H₂O through solid state reaction at 600 °C. Neutron powder diffraction (NPD) data and X-ray absorption spectroscopy revealed the existence of mixed oxidation states for cobalt in this compound, the chemical formula being Bi₁₂(Bi_0.55Co_0.45)O_19.6. The gas sensing properties of Bi₁₂(Bi_0.55Co_0.45)O_19.6 were characterized by alternating current, at 200, 300 and 400 °C. The optimal response was observed at 400 °C, using a frequency of 100 kHz.     

Studies on the gas sensing behaviour of nanosized CuNb2O6 towards ammonia, hydrogen and liquefied petroleum gas

Appreciable changes in resistance of polycrystalline nanosized CuNb₂O₆ upon exposure to reducing gases like hydrogen, liquefied petroleum gas (LPG) and ammonia in ambient atmosphere recognize the material as a gas sensor. Nanosized CuNb₂O₆ synthesized by thermal decomposition of an aqueous precursor solution containing copper nitrate, niobium tartrate and tri-ethanol amine (TEA), followed by calcination at 700 °C for 2 h, has been characterized using X-ray diffraction (XRD) study, transmission electron microscopy (TEM), field-emission scanning electron microscope (FESEM), energy dispersive X-ray (EDX) analysis and Brunauer–Emmett–Teller (BET) surface area measurement. The synthesized CuNb₂O₆ exhibits monoclinic structure with crystallite size of 25 nm, average particle size of 25–40 nm and specific surface area of 55 m² g⁻¹.     

A technique for monitoring SO2 in combustion exhausts: Use of a non-Nernstian sensing element in combination with an upstream catalytic filter

Detection of sulfur dioxide (SO₂) at high temperature (600–750 °C) in the presence of some interferents found in combustion exhausts (NO₂, NO, CO₂, CO, and hydrocarbon (C₃H₆)) is described. The detection scheme involves use of a catalytic filter in front of a non-Nernstian (mixed-potential) sensing element. The catalytic filter was a Ni:Cr powder bed operating at 850 °C, and the sensing elements were pairs of platinum (Pt) and oxide (Ba-promoted copper chromite ((Ba,Cu)_xCr_yO_z) or Sr-modified lanthanum ferrite (LSF)) electrodes on yttria-stabilized zirconia. The Ni:Cr powder bed was capable of reducing the sensing element response to NO₂, NO, CO, and C₃H₆, but the presence of NO₂ or NO (“NO_x”, at 100 ppm by volume) still interfered with the SO₂ response of the Pt–(Ba,Cu)_xCr_yO_z sensing element at 600 °C, causing approximately a 7 mV (20%) reduction in the response to 120 ppm SO₂ and a response equivalent to about 20 ppm SO₂ in the absence of SO₂. The Pt–LSF sensing element, operated at 750 °C, did not suffer from this NO_x interference but at the cost of a reduced SO₂ response magnitude (120 ppm SO₂ yielded 10 mV, in contrast to 30 mV for the Pt-(Ba,Cu)_xCr_yO_z sensing element). The powder bed and Pt–LSF sensing element were operated continuously over approximately 350 h, and the response to SO₂ drifted downward by about 7%, with most of this change occurring during the initial 100 h of operation.     

Sensing mechanism of room temperature CO2 sensors based on primary amino groups

This work reports measurements to elucidate the reaction mechanisms of sensitive materials containing primary amino groups with CO₂. The sensing mechanism is based on their ability to perform reversible acid-base reactions. The effect discussed for most of the previously used sensing layers concerns the formation of bicarbonate species, which requires H₂O as well as an increased temperature. By using work function readout technology an operation at room temperature of the sensing layers is enabled providing satisfying sensor responses in terms of SNR (signal noise ratio) and response time. In contrast to the previously investigated higher operation temperature, the response resulting from a room temperature measurement appears to be dominated by the reversible formation of carbamate, which does not require the presence of water. The presence of carbamate is considered to be the reason of the improved sensing performance of this sensing material at room temperature with work function readout.To confirm this hypothesis, DRIFT-MIR, Raman, XPS and NMR spectroscopy were employed to investigate the formation of species after manufacturing of the sensitive layers. Besides the formation of bicarbonate, the results show a strong indication for carbamate formation.     

Ab initio calculations of NO2 and SO2 chemisorption onto non-polar ZnO surfaces

The authors present an ab initio study of NO₂ and SO₂ chemisorption onto non-polar ZnO and ZnO surfaces with the aim of providing theoretical hints for further developments in gas sensors. From first principles calculations (DFT-GGA approximation), the most relevant surface reduction scenarios are analyzed and, subsequently, considered in the chemisorption study. First, calculations indicate that NO₂ adsorbs avidly onto Zn surface atoms. This is compatible with the oxidizing character of NO₂. Second, results also explain the sensor poisoning by SO₂ adsorption (since this molecule competes with NO₂ for the same adsorption sites) and indicate that poisoning can only be reverted at typical operation temperatures (T ≤ 700 °C) in the case of stoichiometric ZnO surfaces.     

Synthesis of novel SnO2/ZnSnO3 core-shell microspheres and their gas sensing properities

Large-scale novel core-shell structural SnO₂/ZnSnO₃ microspheres were successfully synthesized by a simple hydrothermal method with the help of the surfactant poly(vinyl pyrrolidone) PVP. The as-synthesized samples were characterized using X-ray powder diffraction (XRD), scanning electron microscopy (SEM), transmission electron microscopy (TEM), and high-resolution transmission electron microscopy (HRTEM). The results indicate that the shell was formed by single crystalline ZnSnO₃ nanorods and the core was formed by aggregated SnO₂ nanoparticles. The effects of PVP and hydrothermal time on the morphology of SnO₂/ZnSnO₃ were investigated. A possible formation mechanism of these hierarchical structures was discussed. Moreover, the sensor performance of the prepared core-shell SnO₂/ZnSnO₃ nanostructures to ethanol was studied. The results indicate that the as-synthesized samples exhibited high response and quick response-recovery to ethanol.