Derivation of pan-Arctic soil decomposition rate constant,heterotrophic respiration and NEE using AMSR-E and MODIS data

An approach was developed for regional assessment and monitoring of land-atmosphere carbon dioxide (CO₂) exchange, soil heterotrophic respiration (R _h), and vegetation productivity of Arctic tundra using global satellite remote sensing at optical and microwave wavelengths. C- and X-band brightness temperatures were used from the Advanced Microwave Scanning Radiometer Earth Observing System (AMSR-E) to extract surface wetness and temperature, and MODerate Resolution Imaging Spectroradiometer (MODIS) data were used to derive land cover, Leaf Area Index (LAI), and Net Primary Production (NPP) information. Calibration and validation activities involve comparisons between satellite remote sensing and tundra CO₂ eddy flux towers, and hydroecological process model simulations. Analyses of spatial and temporal anomalies and environmental drivers of land-atmosphere net CO₂ exchange at weekly and annual time steps were conducted. Surface soil moisture and temperature, as detected from satellite remote-sensing observations, were found to be major drivers for spatial and temporal patterns of tundra net ecosystem CO₂ exchange and photosynthetic and respiration processes. Satellite microwave measurements are capable of capturing seasonal variations and regional patterns in tundra soil heterotrophic respiration and CO₂ exchange, while the ability to extract spatial patterns at the scale of surface heterogeneity is limited by the coarse spatial scale of the satellite remote-sensing footprint. The microwave-derived surface temperature and soil moisture were used to estimate net ecosystem carbon exchange (NEE) at the boreal-Arctic region. These were validated using flux tower sites data. Existing satellite-based measurements of vegetation structure (i.e. LAI) and productivity (i.e. Gross Primary Production (GPP) and NPP) from the Aqua/Terra MODIS with the AMSR-E-derived land-surface temperature and soil moisture were used and integrated. Spatially explicit estimates of NEE for the pan-Arctic region at daily, weekly and annual intervals were derived. Comparative analysis of satellite data-derived NEE with measurements from CO₂ eddy flux tower sites and the BIOME-BGC model were carried out and good agreement was found. The comparative analysis is statistically significant with high regression (i.e. R ²?=?0.965), especially in the R _h calculation and the overall NEE regression is 0.478. The results also indicate that the carbon cycle response to climate change is nonlinear and is strongly coupled to Arctic surface hydrology.     

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.     

Sea-air flux of CO2 in the Caribbean Sea estimated using in situ and remote sensing data

Empirical relationships between sea surface carbon dioxide fugacity (fCO₂^sw) and sea surface temperature (SST) were applied to datasets of remotely sensed SST to create fCO₂^sw fields in the Caribbean Sea. SST datasets from different sensors were used, as well as the SST fields created by optimum interpolation of bias corrected AVHRR data. Empirical relationships were derived using shipboard fCO₂^sw data, in situ SST data, and SST data from the remote sensing platforms. The results show that the application of a relationship based on shipboard SST data, on fields of remotely sensed SST yields biased fCO₂^sw values. This bias is reduced if the fCO₂^sw-SST relationships are derived using the same SST data that are used to create the SST fields. The fCO₂^sw fields found to best reproduce observed fCO₂^sw are used in combination with wind speed data from QuikSCAT to create weekly maps of the sea-air CO₂ flux in the Caribbean Sea in 2002. The region to the SW of Cuba was a source of CO₂ to the atmosphere throughout 2002, and the region to the NE was a sink during winter and spring and a source during summer and fall. The net uptake of CO₂ in the region was doubled when potential skin layer effects on fCO₂^sw were taken into account.     

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.     

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.     

Using leaf chlorophyll to parameterize light-use-efficiency within a thermal-based carbon, water and energy exchange model

Chlorophylls absorb photosynthetically active radiation and thus function as vital pigments for photosynthesis, which makes leaf chlorophyll content (C_ab) useful for monitoring vegetation productivity and an important indicator of the overall plant physiological condition. This study investigates the utility of integrating remotely sensed estimates of C_ab into a thermal-based Two-Source Energy Balance (TSEB) model that estimates land-surface CO₂ and energy fluxes using an analytical, light-use-efficiency (LUE) approach to estimating bulk canopy resistance. The LUE model component computes canopy-scale carbon assimilation and transpiration fluxes, internally estimating fluctuations in effective LUE from a nominal (species-dependent) value (LUE_n) in response to short-term variations in environmental conditions. LUE_n, however, may vary on a daily timescale, responding to changes in plant phenology, physiological condition and nutrient status. Therefore, remote sensing methodologies for improving daily estimates of LUE_n have been investigated. Day-to-day variations in LUE_n were assessed for a heterogeneous corn crop field in Maryland, U.S.A. through model optimization with eddy covariance CO₂ flux tower observations. The optimized daily LUE_n values were then compared to gridded estimates of C_ab over the tower flux footprint, retrieved from a canopy reflectance model driven by green, red and near-infrared imagery acquired with an aircraft imaging system. The tower-calibrated LUE_n data were generally well correlated with airborne retrievals of C_ab, and hourly water, energy and carbon flux estimation accuracies from TSEB were significantly improved when using C_ab for delineating spatio-temporal variations in LUE_n. The study highlights the potential synergy between thermal infrared and shortwave reflective wavebands in producing valuable remote sensing data for estimating carbon, water and heat fluxes within a two-source energy balance framework.     

Using ASTER satellite data to calculate riparian evapotranspiration in the Middle Rio Grande,New Mexico

Riparian evapotranspiration (ET) in the Rio Grande Basin in New Mexico, USA is a major component of the hydrological system. Over a period of several years, ET has been measured in selected locations of dense saltcedar and cottonwood vegetation. Riparian vegetation varies in density, species and soil moisture availability, and to obtain accurate measurements, multiple sampling points are needed, making the process costly and impractical. An alternative solution involves using remotely sensed data to estimate ET over large areas. In this study, daily ET values were measured using eddy covariance flux towers installed in areas of saltcedar and cottonwood vegetation. At these sites, remotely sensed satellite data from the National Aeronautics and Space Administration (NASA) Terra Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) were used to calculate the albedo, normalized difference vegetation index (NDVI) and surface temperature. A surface energy balance model was used to calculate ET values from the ASTER data, which were available for 7 days in the year. Comparison between the daily ET values of saltcedar and cottonwood measured from the flux towers and calculated from remote sensing resulted in a mean square error (MSE) of 0.16 and 0.37 mm day^?1, respectively. The regional map of ET generated from the remote sensing data demonstrated considerable variation in ET, ranging from 0 to 9.8 mm day^?1, with a mean of 5.5 mm day^?1 and standard deviation of 1.85 mm day^?1 (n = 427481 pixels) excluding open water. This was due to variations in plant variety and density, soil type and moisture availability, and the depth to water table.     

Modelling carbon fluxes of different forests by coupling a remote-sensing model with an ecosystem process model

A new model (GLOPEM-CEVSA) to determine terrestrial carbon budgets was developed by coupling remote sensing with ecosystem process simulation, and was validated with reference to the carbon fluxes of three forests. MODIS FPAR (MOD15A2 product) was applied together with meteorological data on flux towers. The seasonal variances of modelled gross primary production and ecosystem respiration were significantly correlated with observed values (correlation coefficient, r > 0.9). The seasonal dynamics of the modelled net ecosystem production over the plant-growth season showed significant agreement with observed values with a r range of 0.64 to 0.87. This work demonstrates the potential of GLOPEM-CEVSA to quantify the spatial patterns and temporal dynamics of terrestrial ecosystem carbon sources and sinks with consideration of the spatial heterogeneity of ecosystems based on remote sensing.     

Near‐noon albedo values of alfalfa and tall fescue grass derived from multispectral data

A remote sensing approach was applied to estimate near‐noon values of shortwave albedo (α), the fraction of solar radiation reflected by a surface, for alfalfa and tall fescue grass at Kimberly, Idaho. The approach was based on the (P/T) ratio, which is the ratio of the partial radiation (P) sensed by a multi‐band radiometer and the total incident radiation (T) in a given wavelength range. It was found that instead of being constant, as previously suggested, the upward component of the (P/T) ratio under clear‐sky conditions [(P/T)_u] followed a logistic growth function of solar altitude angle (Λ_z) for both crops (r ² = 0.84). The downward component [(P/T)_d], on the other hand, linearly increased with Λ_z (r ² = 0.83). By applying the (P/T) ratio methodology, using variable ratios, it was found that the diurnal pattern of clear‐sky α for both crops followed a decreasing function of Λ_z (r ² = 0.80). Near‐noon α values for alfalfa estimated using remote sensing were linearly related to plant canopy height (h) (r ² = 0.92), but not to Λ_z. For grass, on the other hand, the near‐noon α values obtained by remote sensing were not correlated with either h or Λ_z. The near‐noon α values for alfalfa obtained with remote sensing deviated considerably from those estimated using an empirical function of day of the year (DOY). For alfalfa, the near‐noon net radiation (R _n) values calculated using α values derived by remote sensing were better correlated to measured R _n values than those obtained using α estimated as a function of DOY. For grass, the α values derived from remote sensing did not significantly improve the accuracy of the calculated near‐noon R _n compared with using α values estimated as a function of Λ_z.     

Parallel adjustments in vegetation greenness and ecosystem CO2 exchange in response to drought in a Southern California chaparral ecosystem

Some form of the light use efficiency (LUE) model is used in most models of ecosystem carbon exchange based on remote sensing. The strong relationship between the normalized difference vegetation index (NDVI) and light absorbed by green vegetation make models based on LUE attractive in the remote sensing context. However, estimation of LUE has proven problematic since it varies with vegetation type and environmental conditions. Here we propose that LUE may in fact be correlated with vegetation greenness (measured either as NDVI at constant solar elevation angle, or a red edge chlorophyll index), making separate estimates of LUE unnecessary, at least for some vegetation types. To test this, we installed an automated tram system for measurement of spectral reflectance in the footprint of an eddy covariance flux system in the Southern California chaparral. This allowed us to match the spatial and temporal scales of the reflectance and flux measurements and thus to make direct comparisons over time scales ranging from minutes to years. The 3-year period of this study included both “normal” precipitation years and an extreme drought in 2002. In this sparse chaparral vegetation, diurnal and seasonal changes in solar angle resulted in large variation in NDVI independent of the actual quantity of green vegetation. In fact, one would come to entirely different conclusions about seasonal changes in vegetation greenness depending on whether NDVI at noon or NDVI at constant solar elevation angle were used. Although chaparral vegetation is generally considered “evergreen”, we found that the majority of the shrubs were actually semi-deciduous, leading to large seasonal changes in NDVI at constant solar elevation angle. LUE was correlated with both greenness indices at the seasonal timescale across all years. In contrast, the relationship between LUE and PRI was inconsistent. PRI was well correlated with LUE during the “normal” years but this relationship changed dramatically during the extreme drought. Contrary to expectations, none of the spectral reflectance indices showed consistent relationships with CO₂ flux or LUE over the diurnal time-course, possibly because of confounding effects of sun angle and stand structure on reflectance. These results suggest that greenness indices can be used to directly estimate CO₂ exchange at weekly timescales in this chaparral ecosystem, even in the face of changes in LUE. Greenness indices are unlikely to be as good predictors of CO₂ exchange in dense evergreen vegetation as they were in the sparse, semi-deciduous chaparral. However, since relatively few ecosystems are entirely evergreen at large spatial scales or over long time spans due to disturbance, these relationships need to be examined across a wider range of vegetation types.     

Application of satellite remote sensing techniques for estimating air–sea CO2 fluxes in Hudson Bay,Canada during the ice-free season

The role of coastal seas as either a sink or a source of CO₂ is subject to a great deal of uncertainty. This uncertainty largely arises from a lack of observations in the coastal zones. Remote sensing offers an avenue for expanding these observations by allowing for the extrapolation of relatively limited data sets of dissolved CO₂ (pCO_2sw). In this paper, predictive algorithms for pCO_2sw that could be applied to remote sensing products were created from a field data set collected from September–October, 2005 in Hudson Bay, Canada. The field data showed that an effective pCO_2sw interpolation algorithm could be created using sea surface temperature (SST) as a predictor, and that a slight improvement of the algorithm could be achieved if measurements of absorption due to coloured dissolved organic material (a_CDOM) were included. Unfortunately, satellite retrievals of a_CDOM did not match well with in situ observations, and so only SST (obtained from the MODIS Aqua sensor) was used to create monthly maps of pCO_2sw for the period of August–October. To estimate fluxes of CO₂, constructed surfaces of pCO_2sw were combined with estimates of gas transfer velocity derived from QuikSCAT wind retrievals, and pCO_2air based on field observations. The results of these calculations revealed that Hudson Bay acts as a source of CO₂ during August and September, but reverts to a sink of CO₂ in October as the water temperature decreases. Overall, a positive flux of 1.60 TgC was estimated for the region during the ice-free season. This result is in contrast to most Arctic or sub-Arctic continental shelf seas, where usually strong absorptions of CO₂ are observed.     

Mapping soil moisture in the central Ebro river valley (northeast Spain) with Landsat and NOAA satellite imagery: a comparison with meteorological data

This paper analyses and maps the spatial distribution of soil moisture using remote sensing: National Oceanic and Atmospheric Administration (NOAA) Advanced Very High Resolution Radiometer (AVHRR) and Landsat-Enhanced Thematic Mapper (ETM+) images. The study was carried out in the central Ebro river valley (northeast Spain), and examines the spatial relationships between the distribution of soil moisture and several meteorological and geographical variables following a long, intense dry period (winter 2000). Soil moisture estimates were obtained using thermal, visible and near-infrared data and by applying the ‘triangle method’, which describes relationships between surface temperature (T_s ) and fractional vegetation cover (F_r ). Low differences were found between the soil moisture estimates obtained using AVHRR and ETM+ sensors. Soil moisture estimated using remote sensing is close to estimations obtained from climate indices. This fact, and the high similarity between estimations of both sensors, suggests the reasonable reliability of soil moisture remote sensing estimations. Moreover, in estimations from both sensors the spatial distribution of soil moisture was largely accounted for by meteorological variables, mainly precipitation in the dry period. The results indicate the high reliability of remote sensing for determining areas affected by water deficits and for quantifying drought intensity.     

Analysis of energy fluxes and land surface parameters in a grassland ecosystem: a remote sensing perspective

A remote sensing‐based land surface characterization and flux estimation study was conducted using Landsat data from 1997 to 2003 on two grazing land experimental sites located at the Agricultural Research Services (ARS), Mandan, North Dakota. Spatially distributed surface energy fluxes [net radiation (R _n), soil heat flux (G), sensible heat (H), latent heat (LE)] and surface parameters [emissivity (ε), albedo (α), normalized difference vegetation index (NDVI) and surface temperature (T _sur)] were estimated and mapped at a pixel level from Landsat images and weather information using the Surface Energy Balance Algorithm for Land (SEBAL) procedure as a function of grazing land management: heavily grazed (HGP) and moderately grazed pastures (MGP). Energy fluxes and land surface parameters were mapped and comparisons were made between the two sites. Over the study period, H, ε and T _sur from HGP were higher by 6.7%, 18.2% and 2.9% than in MGP, respectively. The study also showed that G, LE and NDVI were higher by 1.3%, 1.6% and 7.4% for MGP than in HGP, respectively. The results of this study are beneficial in understanding the trends of land surface parameters, energy and water fluxes as a function of land management.     

Cover

Abstract

The percentage of polarized visible light (PVL) reflected from a surface has largely been neglected as a potential source of remotely sensed data. Methods of displaying PVL data appropriate to environmental remote sensing are similarly ill-developed. A method of presenting such data in a two-dimensional feature space is described which allows the important polarization parameters to be shown in a clear and informative manner. This method of display also highlights some of the limitations inherent in the use of %PVL derived from measurements of the Stokes parameters I and Q.     

Remote sensing based estimation of evapotranspiration rates

A remote sensing based method is presented for calculating evapotranspiration rates (λE) using standard meteorological field data and radiometric surface temperature recorded for bare soil, maize and wheat canopies in Denmark. The estimation of λE is achieved using three equations to solve three unknowns; the atmospheric resistance (r_ae ), the surface resistance (r_s ) and the vapour pressure at the surface (e_s ) where the latter is assessed using an empirical expression. The method is applicable, without modification, to dense vegetation and moist soil, but for a dry bare soil, where the effective source of water vapour is below the surface, the temperature of the evaporating front (T_s *) can not be represented by the measured surface temperature (T_s ). In this case (T_s -T_s *) is assessed as a linear function of the difference between surface temperature and air temperature. The calculated λE is comparable to latent heat fluxes recorded by the eddy covariance technique.     

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.     

A simple algorithm to estimate sensible heat flux from remotely sensed MODIS data

Sensible heat flux (H) has a large impact on energy exchange between the surface and the atmosphere and, thus, affects climate change and climatic and hydrological modelling. In the past, remote sensing of H has been a major area of interest and, as a result, various methods have been established for its retrieval. However, large discrepancies between measured and simulated values of H have been observed over land surfaces because of various assumptions and simplifications. This article presents a generalized algorithm for the estimation of sensible heat flux that is suitable for a wide range of atmospheric and terrestrial conditions from Moderate Resolution Imaging Spectroradiometer (MODIS) data. Standard built-in atmospheric profiles in Fast Atmospheric Signature Code (FASCODE) together with atmospheric conditions obtained by periodic radio sounding, once a week, performed at the Broglio Space Centre in Malindi, Kenya, were used in simulating MODIS data at 11.03 and 12.02 μm wavelengths using PcLnWin software. This new approach improves the form of the Mito algorithm, developed to determine surface temperature, by removing some of the assumptions underlying the algorithm – for example, the assumption that air temperature T _a is approximately equal to surface temperature T _s. The resulting bulk aerodynamic resistance equation allows the formulation of a general algorithm for the determination of H, which takes into account the surface emittance effect, water vapour column (WVC), canopy properties, air temperature and different atmospheric stabilities. Unlike other conventional methods developed earlier for the determination of H, a prior knowledge of surface temperature as an auxiliary input is not necessary in this new algorithm. The estimates of sensible heat flux derived from MODIS using the proposed algorithm compared well with in situ measurements, giving a good correlation coefficient of r?=?0.9.     

Normalized difference spectral indices for estimating photosynthetic efficiency and capacity at a canopy scale derived from hyperspectral and CO2 flux measurements in rice

We explored simple and useful spectral indices for estimating photosynthetic variables (radiation use efficiency and photosynthetic capacity) at a canopy scale based on seasonal measurements of hyperspectral reflectance, ecosystem CO₂ flux, and plant and micrometeorological variables. An experimental study was conducted over the simple and homogenous ecosystem of an irrigated rice field. Photosynthetically active radiation absorbed by the canopy (APAR), the canopy absorptivity of APAR (fAPAR), net ecosystem exchange of CO₂ (NEE_CO2) gross primary productivity (GPP), photosynthetic capacity at the saturating APAR (P_max), and three parameters of radiation use efficiency (ε_N: NEE_CO2/APAR; ε_G: GPP/APAR; φ: quantum efficiency) were derived from the data set. Based on the statistical analysis of relationships between these ecophysiological variables and reflectance indicators such as normalized difference spectral indices (NDSI[i,j]) using all combinations of two wavelengths (i and j nm), we found several new indices that would were more effective than conventional spectral indices such as photochemical reflectance index (PRI) and normalized difference vegetation index (NDVI = NDSI[near-infrared, red]). ε_G was correlated well with NDSI[710, 410], NDSI[710, 520], and NDSI[530, 550] derived from nadir measurements. φ was best correlated with NDSI[450, 1330]. NDSI[550, 410] and NDSI[720, 420] had a consistent linear relationships with fAPAR throughout the growing season, whereas conventional indices such as NDVI showed very different relationships before and after heading. Off-nadir measurements were more closely related to the efficiency parameters than nadir measurements. Our results provide useful insights for assessing plant productivity and ecosystem CO₂ exchange, using a wide range of available spectral data as well as useful information for designing future sensors for ecosystem observations.     

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.     

A new model of gross primary productivity for North American ecosystems based solely on the enhanced vegetation index and land surface temperature from MODIS

Many current models of ecosystem carbon exchange based on remote sensing, such as the MODIS product termed MOD17, still require considerable input from ground based meteorological measurements and look up tables based on vegetation type. Since these data are often not available at the same spatial scale as the remote sensing imagery, they can introduce substantial errors into the carbon exchange estimates. Here we present further development of a gross primary production (GPP) model based entirely on remote sensing data. In contrast to an earlier model based only on the enhanced vegetation index (EVI), this model, termed the Temperature and Greenness (TG) model, also includes the land surface temperature (LST) product from MODIS. In addition to its obvious relationship to vegetation temperature, LST was correlated with vapor pressure deficit and photosynthetically active radiation. Combination of EVI and LST in the model substantially improved the correlation between predicted and measured GPP at 11 eddy correlation flux towers in a wide range of vegetation types across North America. In many cases, the TG model provided substantially better predictions of GPP than did the MODIS GPP product. However, both models resulted in poor predictions for sparse shrub habitats where solar angle effects on remote sensing indices were large. Although it may be possible to improve the MODIS GPP product through improved parameterization, our results suggest that simpler models based entirely on remote sensing can provide equally good predictions of GPP.