Effects of sub-pixel heterogeneity on the retrieval of soil moisture from passive microwave radiometry

A simple formulation relating the L-band microwave brightness temperature detected by a passive microwave radiometer to the near surface soil moisture was developed using MICRO-SWEAT, a coupled microwave emission model and soil-vegetation-atmosphere-transfer (SVAT) scheme. This simple model provides an ideal tool with which to explore the impact of sub-pixel heterogeneity on the retrieval of soil moisture from microwave brightness temperatures. In the case of a bare soil pixel, the relationship between apparent emissivity and surface soil moisture is approximately linear, with the clay content of the soil influencing just the intercept of this relationship. It is shown that there are no errors in the retrieved soil moisture from a bare soil pixel that is heterogeneous in soil moisture and texture. However, in the case of a vegetated pixel, the slope of the relationship between apparent emissivity and surface soil moisture decreases with increasing vegetation. Therefore for a pixel that is heterogeneous in vegetation and soil moisture, errors can be introduced into the retrieved soil moisture. Generally, under moderate conditions, the retrieved soil moisture is within 3% of the actual soil moisture. Examples illustrating this discussion use data collected during the Southern Great Plains '97 Experiment (SGP97).     

Modelling surface energy fluxes over maize using a two-source patch model and radiometric soil and canopy temperature observations

Models estimating surface energy fluxes over partial canopy cover with thermal remote sensing must account for significant differences between the radiometric temperatures and turbulent exchange rates associated with the soil and canopy components of the thermal pixel scene. Recent progress in separating soil and canopy temperatures from dual angle composite radiometric temperature measurements has encouraged the development of two-source (soil and canopy) approaches to estimating surface energy fluxes given observations of component soil and canopy temperatures. A Simplified Two-Source Energy Balance (STSEB) model has been developed using a “patch” treatment of the surface flux sources, which does not allow interaction between the soil and vegetation canopy components. A simple algorithm to predict the net radiation partitioning between the soil and vegetation is introduced as part of the STSEB patch modelling scheme. The feasibility of the STSEB approach under a full range in fractional vegetation cover conditions is explored using data collected over a maize (corn) crop in Beltsville Maryland, USA during the 2004 summer growing season. Measurements of soil and canopy component temperatures as well as the effective composite temperature were collected over the course of the growing season from crop emergence to cob development. Comparison with tower flux measurements yielded root-mean-square-difference values between 15 and 50 W m^− 2 for the retrieval of the net radiation, soil, sensible and latent heat fluxes. A detailed sensitivity analysis of the STSEB approach to typical uncertainties in the required inputs was also conducted indicating greatest model sensitivity to soil and canopy temperature uncertainties with relative errors reaching ∼ 30% in latent heat flux estimates. With algorithms proposed to infer component temperatures from bi-angular satellite observations, the STSEB model has the capability of being applied operationally.     

The impact of soil reflectance on the quantification of the green vegetation fraction from NDVI

The green vegetation fraction (Fg) is an important climate and hydrologic model parameter. A common method to calculate Fg is to create a simple linear mixing model between two NDVI endmembers: bare soil NDVI (NDVI_o) and full vegetation NDVI (NDVI_∞). Usually it is assumed that NDVI_o is close to zero (NDVI_o ∼ 0.05) and is generally chosen from the lowest observed NDVI values. However, the mean soil NDVI computed from 2906 samples is much larger (NDVI = 0.2) and is highly variable (standard deviation = 0.1). We show that the underestimation of NDVI_o yields overestimations of Fg. The largest errors occur in grassland and shrubland areas. Using parameters for NDVI_o and NDVI_∞ derived from global scenes yields overestimations of Fg that are larger than 0.2 for the majority of U.S. land cover types when pixel NDVI values are 0.2 < NDVI_pixel < 0.4. When using conterminous U.S. scenes to derive NDVI_o and NDVI_∞, the overestimation is less (0.10-0.17 for 0.2 < NDVI_pixel < 0.4). As a result, parts of the conterminous U.S. are affected at different times of the year depending on the local seasonal NDVI cycle. We propose using global databases of NDVI_o along with information on historical NDVI_pixel values to compute a statistically most-likely estimate of Fg. Using in situ measurements made at the Sevilleta LTER, we show that this approach yields better estimates of Fg than using global invariant NDVI_o values estimated from whole scenes. At the two studied sites, the Fg estimate was adjusted by 52% at the grassland and 86% at the shrubland. More significant advances will require information on spatial distribution of soil reflectance.     

Relating spatial patterns of fractional land cover to savanna vegetation morphology using multi-scale remote sensing in the Central Kalahari

Understanding, monitoring, and managing savanna ecosystems requires characterizing both functional and structural properties of vegetation. From a functional perspective, in savannas, quantitative estimation of fractional cover of photosynthetic vegetation (f_PV), non-photosynthetic vegetation (f_NPV), and bare soil (f_BS) is important as it relates to carbon dynamics and ecosystem function. On the other hand, vegetation morphology classes describe the structural properties of the ecosystem. Due to high functional diversity and structural heterogeneity in savannas, accurately characterizing both these properties using remote sensing is methodologically challenging. While mapping both fractional cover and vegetation morphology classes are important research themes within savanna remote sensing, very few studies have considered systematic investigation of their spatial association across different spatial resolutions. Focusing on the semi-arid savanna ecosystem in the Central Kalahari, this study utilized f_PV, f_NPV, and f_BS derived in situ and estimated from spectral unmixing of high- (GeoEye-1), medium- (Landsat TM), and coarse- (MODIS) spatial resolution imagery to investigate: (i) the impact of reducing spatial resolution on both magnitude and accuracy of fractional cover; and (ii) how fractional-cover magnitude and accuracy are spatially associated with savanna vegetation morphology classes. Endmembers for Landsat TM and GeoEye-1 were derived from the image based on purity measures; for MODIS (MCD43A4), the challenge of finding spectral endmembers was addressed following an empirical multi-scale hierarchical approach. GeoEye-1-derived fractional estimates showed comparatively closest agreement with in situ measurements and were used to evaluate Landsat TM and MODIS. Overall results indicate that increasing pixel size caused consistent increases in variance of and error in fractional-cover estimates. Even at coarse spatial resolution, f_PV was estimated with higher accuracy compared with f_NPV and f_BS. Assessment considering vegetation morphology of samples revealed both morphology- and cover-specific differences in accuracy. At larger pixel sizes, in areas with dominant woody vegetation, f_PV was overestimated at the cost of mainly underestimating f_BS; in contrast, in areas with dominant herbaceous vegetation, f_NPV was overestimated with a corresponding underestimation of both f_PV and f_BS. These results underscore that structural and functional heterogeneity in semi-arid savanna both impact retrieval of fractional cover, suggesting that comprehensive remote sensing of savannas needs to take both structure and cover into account.     

Soil moisture estimation in a semiarid rangeland using ERS-2 and TM imagery

Soil moisture is important information in semiarid rangelands where vegetation growth is heavily dependent on the water availability. Although many studies have been conducted to estimate moisture in bare soil fields with Synthetic Aperture Radar (SAR) imagery, little success has been achieved in vegetated areas. The purpose of this study is to extract soil moisture in sparsely to moderately vegetated rangeland surfaces with ERS-2/TM synergy. We developed an approach to first reduce the surface roughness effect by using the temporal differential backscatter coefficient (Δσ_wet-dry⁰). Then an optical/microwave synergistic model was built to simulate the relationship among soil moisture, Normalized Difference Vegetation Index (NDVI) and Δσ_wet-dry⁰. With NDVI calculated from TM imagery in wet seasons and Δσ_wet-dry⁰ from ERS-2 imagery in wet and dry seasons, we derived the soil moisture maps over desert grass and shrub areas in wet seasons. The results showed that in the semiarid rangeland, radar backscatter was positively correlated to NDVI when soil was dry (m_v<10%), and negatively correlated to NDVI when soil moisture was higher (m_v>10%). The approach developed in this study is valid for sparse to moderate vegetated areas. When the vegetation density is higher (NDVI>0.45), the SAR backscatter is mainly from vegetation layer and therefore the soil moisture estimation is not possible in this study.     

Sub‐pixel reflectance unmixing in estimating vegetation water content and dry biomass of corn and soybeans cropland using normalized difference water index (NDWI) from satellites

Estimating vegetation cover, water content, and dry biomass from space plays a significant role in a variety of scientific fields including drought monitoring, climate modelling, and agricultural prediction. However, getting accurate and consistent measurements of vegetation is complicated very often by the contamination of the remote sensing signal by the atmosphere and soil reflectance variations at the surface. This study used Landsat TM/ETM+ and MODIS data to investigate how sub‐pixel atmospheric and soil reflectance contamination can be removed from the remotely sensed vegetation growth signals. The sensitivity of spectral bands and vegetation indices to such contamination was evaluated. Combining the strengths of atmospheric models and empirical approaches, a hybrid atmospheric correction scheme was proposed. With simplicity, it can achieve reasonable accuracy in comparison with the 6S model. Insufficient vegetation coverage information and poor evaluation of fractional sub‐pixel bare soil reflectance are major difficulties in sub‐pixel soil reflectance unmixing. Vegetation coverage was estimated by the Normalized Difference Water Index (NDWI). Sub‐pixel soil reflectance was approximated from the nearest bare soil pixel. A linear reflectance mixture model was employed to unmix sub‐pixel soil reflectance from vegetation reflectance. Without sub‐pixel reflectance contamination, results demonstrate the true linkage between the growth of sub‐pixel vegetation and the corresponding change in satellite spectral signals. Results suggest that the sub‐pixel soil reflectance contamination is particularly high when vegetation coverage is low. After unmixing, the visible and shortwave infrared reflectances decrease and the near‐infrared reflectances increase. Vegetation water content and dry biomass were estimated using the unmixed vegetation indices. Superior to the NDVI and the other NDWIs, the SWIR (1650 nm) band‐based NDWI showed the best overall performance. The use of the NIR (1240 nm), which is a unique band of MODIS, was also discussed.     

Thermal infrared inverse model for component temperatures of mixed pixels

Multiangular remote sensing data can be used to retrieve land surface component temperatures, which will have a broad application in the future. For higher resolution pixels of satellite radiometers, the component temperatures may be separated adequately by some methods. However, for coarse resolution pixels that contain a mixture of vegetation and bare soil, the component temperatures may not be retrieved robustly by traditional inversion methods. In this study, a thermal model-based algorithm was developed for mixed pixels. A simulation method was implemented to assess the performance of the algorithm. The method consisted of extensive radiative transfer simulations under a wide variety of Leaf Area Index (LAI) values in the vegetation part of directional thermal infrared (TIR) radiation, vegetation and soil emissivity, vegetation and soil temperatures, bare soil area ratio and downwelling longwave atmospheric radiation. The results indicate that the inversion error of the component temperatures does not exceed 0.5° when LAI values are less than 6.0. A field experiment was also conducted to assess the accuracy of the model. The experimental results indicate that even if the differences between the nadir and off-nadir radiative temperatures over a mixed pixel are small, the model can still determine the component temperatures accurately. A sensitivity analysis shows that an accuracy of less than 10% for LAI in the vegetation part and the bare soil area ratio is required to achieve a precision of 1 K for the component temperatures derived. An error of 1 K in the radiometric temperature leads to an error of 1 K in the component temperatures retrieved.     

A modified vegetation backscattering model for leaf area index retrieval from SAR time series

In this study, a semi-empirical modified vegetation backscattering model was developed to retrieve leaf area index (LAI) based on multi-temporal Radarsat-2 data and ground observations collected in China. This model combined the contribution of the vegetation and bare soil at the pixel level by adding vegetation coverage and the influence of bare soil on the total backscatter coefficients. Then, a lookup table algorithm was applied to calculate the value of vegetation water content and retrieve the LAI based on the linear relationship between the vegetation water content and LAI. The results indicated that the modified model was effective in evaluating and reproducing the total backscatter coefficients. Meanwhile, the LAI retrieval was well conducted with coefficient of determination (R²) and root mean square error (RMSE) of 89% and 0.19 m² m^?2, respectively. Additionally, this method offers insight into the required application accuracy of LAI retrieval in the agricultural regions.     

Analysis of normalized difference and surface temperature observations over southeastern Australia

Abstract

Relations between radiative surface temperature (T_R) and visible and near-infrared reflectances expressed as the normalized difference (ND) from a Landsat Thematic Mapper scene were analysed to study the heat balance of agriculture and native evergreen forests in southeastern Australia. The scene was at 0922 h (local time) during late spring (15 October 1986) with phenology of winter annual species between flowering and grain filling. Factors determining the slope of, and residual scatter about, the T_R)/ND relationships were analysed using a coupled two-layer soil-vegetation model of the surface heat balance. Inverse linear relationships were found between T_R), and ND for agriculture, but not for forests. This was a result of a wide range of ND and T_R) values in agricultural areas caused by wide variations in fractional vegetation cover. The relationships between ND and fractional vegetation cover was not general, thus, when forest and agricultural data were combined, the lower near-infrared reflectance of forests (16-9 per cent) compared with agricultural crops (29-0 per cent) resulted in forest data falling below the regression line for agriculture. From the agricultural relations, 60 to 70 per cent of the variation in T_R) was explained by ND, indicating that fractional vegetation cover was the dominant factor determining T_R). Residual variability of T_R) was attributed to spatial variability in ambient temperature, rates of soil evaporation and variations in stage of phenological development affecting stoma-tal resistance. Energy-balance analysis of the effect of soil water availability on the slope of the _R)‘ND relationship indicated opposite effects depending on whether the reduction in evaporation was from soil or from vegetation. Thus, the generality of using the slope of the T_R)/ND relationship to predict surface resistance to evaporation may be limited. It was concluded that extraction of information contained in T_R) and ND data for regional estimation of evaporation requires the separation of T_R) into soil and vegetation temperatures and an alternative to ND that relates more generally to fractional vegetation cover.     

On the correlation of indices of vegetation and surface temperature over south-eastern Australia

Abstract

Relationships between radiant surface temperature (T _R) and vegetation indices for scenes with equal areas of forest and agricultural land use were studied using a Landsat Thematic Mapper (TM) scene during spring and a NOAA-Advanced Very High Resolution Radiometer (AVHRR) scene during summer. The relationships between TR and the Normalized Difference (ND) index of vegetation for agricultural land use were different from those for forests. At the same T _R, the forests had lower near infrared reflectance. This caused the ND of forests to fall below the T _R/ND relationships formed by agricultural land use. This difference between forest and agricultural land use did not exist when visible reflectance (VIS) was used as the index of vegetation. When the two land use systems were combined VIS accounted for about 86 per cent of the variance in T _R. The slope of the relationships between VIS and T _R differed for TM and AVHRR scenes. This was explained by differences in the T _R and VIS reflectance of surfaces with near-zero evaporation. These surfaces were predominantly bare soil in the TM scene and senesced vegetation in the AVHRR scene.     

Spectral modelling of multicomponent landscapes in the Sahel

Abstract

Certain landscapes in the Sahel and elsewhere consist of a ‘checkerboard’ arrangement of vegetated and non-vegetated areas in which there may be several spectrally distinct vegetation and bare ground components. When individual components form large spatially coherent patches, and the vertical dimension of the vegetation is small, spectral interactions between components are negligible. The influence of any one component on the average reflectance of the landscape can then be described by its spectral properties and relative area using simple additive mixture models. These models can be extended to the vegetation indices. The spatial average normalized difference vegetation index (NDVI) is a function of the brightness (red plus near-infrared reflectances), the NDVI and the fractional cover of the components. In landscapes where soil and vegetation can be considered the only components, the NDVI-brightness model can be inverted to obtain the NDVI of the vegetation. Aerial photoradiometer data from Mali, West Africa were used to determine the red and near-infrared component reflectances of soil and vegetation. The derived soil component reflectances were well correlated with ground measurements. The relationship between the vegetation component NDVI and plant cover was better than between the NDVI of the entire landscape and plant cover. The usefulness of this modelling approach depends on the existence of clearly distinguishable landscape components. The method resolves the spectral properties of individual components, but the vegetation component, while free of the effect of bare ground components, is still affected by the underlying soil.     

Air temperature estimation with MSG-SEVIRI data: Calibration and validation of the TVX algorithm for the Iberian Peninsula

Air temperature can be estimated from remote sensing by combining information in thermal infrared and optical wavelengths. The empirical TVX algorithm is based on an estimated linear relationship between observed Land Surface Temperature (LST) and a Spectral Vegetation Index (NDVI). Air temperature is assumed to be equal to the LST corresponding to the effective full vegetation cover, and is found by extrapolating the line to a maximum value of NDVI_max. The algorithm has been tested and reported in the literature previously. However, the effect of vegetation types and climates and the potential variation in NDVI of the effective full cover has not been subject for investigation. The present study proposes a novel methodology to estimate NDVI_max that uses observed air temperature to calibrate the NDVI_max for each vegetation type. To assess the validity of this methodology, we have compared the accuracy of estimates using the new NDVI_max and the previous NDVI_max that have been proposed in literature with MSG-SEVIRI images in Spain during the year 2005. In addition, a spatio-temporal assessment of residuals has been performed to evaluate the accuracy of retrievals in terms of daily and seasonal variation, land cover, landscape heterogeneity and topography. Results showed that the new calibrated NDVI_max perform well, with a Mean Absolute Error ranging between 2.8 °C and 4 °C. In addition, vegetation-specific NDVI_max improve the accuracy compared with a unique NDVI_max.     

Estimating sub-pixel temperatures using the triangle algorithm

In water-deficient areas, water resource management requires evapotranspiration at high spatiotemporal resolution – an impossible situation given the trade-off between spatial and temporal resolutions in space-borne systems. Some researchers have suggested sharpening the Moderate Resolution Imaging Spectroradiometer (MODIS) land-surface temperature product with a resolution from 1 km to 250 m and a functional relationship between surface temperature (T _r) and normalized difference vegetation index (NDVI). Evapotranspiration at 250 m resolution can be obtained once every few days using this technique. Based on the interpretation of the triangular T _r–NDVI space and assuming uniform soil moisture conditions in a coarse pixel, this paper suggests an alternative algorithm – the triangle algorithm – for sharpening. The triangle algorithm was tested using Landsat 7 Enhanced Thematic Mapper Plus (ETM+) data from an arid zone. Sharpened surface temperatures and reference temperatures were compared at 60 m and 240 m resolutions. Root mean square errors with the triangle algorithm are smaller than those with a functional relationship between T _r and NDVI. This paper will also discuss the impact of soil moisture variations in the coarse pixel on the triangle algorithm. Finally we should mention that the triangle algorithm only applies to regions with non-stressed vegetation canopies.     

Ground-based method for measuring thermal infrared effective emissivities: Implications and perspectives on the measurement of land surface temperature from satellite data

Thermal infrared emissivity is an important parameter for surface characterization and for determining surface temperature. The field-based measurements for ground and vegetation emissivities in 8-14 w m waveband were performed with an emissivity box. A theoretical analysis was carried out using the box and a correcting factor has been determined. The average value for thermal band emissivity of the exposed bare soil was found to be around 0.909; the average value measured for most of the varieties of vegetation present were in the range of 0.980-0.985. A theoretical model is used for obtaining effective emissivity in the 8-14 w m region from Advanced Very High Resolution Radiometer (AVHRR) data considering the proportion of vegetation cover in a pixel and the field-measured emissivity values. The error of the methodology is found to be within 1.5%. Narrow band emissivities for AVHRR channels 4 and 5 have been derived from the emissivity values in the 8-14 w m waveband. The surface temperature has been derived from AVHRR data using a split-window algorithm as a function of emissivities derived in narrow bands. The split-window algorithm accounted for absorption effects of the atmosphere by incorporating the water vapour concentration measured in the campaign. A good agreement was obtained between the satellite-derived surface temperature and the in situ observations. The result suggest that the methodology allows us to derive land surface temperature with an accuracy better than 1.5° C provided the surface emissivity is known. The paper describes the field-based emissivity measurement and approach for deriving surface temperature over land surface.     

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.     

Estimating subpixel surface temperatures and energy fluxes from the vegetation index-radiometric temperature relationship

Routine (i.e., daily to weekly) monitoring of surface energy fluxes, particularly evapotranspiration (ET), using satellite observations of radiometric surface temperature has not been feasible at high pixel resolution (i.e., ∼10¹-10² m) because of the low frequency in satellite coverage over the region of interest (i.e., approximately every 2 weeks). Cloud cover further reduces the number of useable observations of surface conditions resulting in high-resolution satellite imagery of a region typically being available once a month, which is not very useful for routine ET monitoring. Radiometric surface temperature observations at ∼1- to 5-km pixel resolution are available multiple times per day from several weather satellites. However, this spatial resolution is too coarse for estimating ET from individual agricultural fields or for defining variations in ET due to land cover changes. Satellite data in the visible and near-infrared wavelengths, used for computing vegetation indices, are available at resolutions an order of magnitude smaller than in the thermal-infrared, and hence provide higher resolution information on vegetation cover conditions. A number of studies have exploited the relationship between vegetation indices and radiometric surface temperature for estimating model parameters used in computing spatially distributed fluxes and available moisture. In this paper, the vegetation index-radiometric surface temperature relationship is utilized in a disaggregation procedure for estimating subpixel variation in surface temperature with aircraft imagery collected over the US Southern Great Plains. The disaggregated surface temperatures estimated by this procedure are compared to actual observations at this subpixel resolution. In addition, a remote sensing-based energy balance model is used to compare output using actual versus estimated surface temperatures over a range of pixel resolutions. From these comparisons, the utility of the surface temperature disaggregation technique appears to be most useful for estimating subpixel surface temperatures at resolutions corresponding to length scales defining agricultural field boundaries across the landscape.     

Accuracy and applicability of linear spectral unmixing in delineating potential erosion areas in tropical watersheds

This study was undertaken to assess the accuracy of linear spectral unmixing (LSU) in estimating fractional abundance of land cover components and to examine its applicability in delineating potential erosion areas in tropical watershed. Five image end‐members (mixed vegetation, grass, Acacia auriculiformis, bare soil and water/shadow) were selected and used in different combinations in unmixing Landsat Enhanced Thematic Mapper (ETM) into fraction images. The accuracy assessment was conducted by comparing the land cover abundance estimates derived from unmixing with the land cover abundance measured from field‐validated classified QuickBird imagery. Good agreement was obtained using a four‐end‐member combination in which shadow was eliminated. The results suggest that LSU could be implemented for soil erosion detection. In general, soil erosion increases when vegetation cover decreases; hence, we used the fraction images to derive a bare soil/vegetation cover ratio and used that as a simple indicator to map high potential erosion areas. Comparison with field assessment of actual erosion levels in the study area showed that the technique is effective in identifying areas on which erosion control efforts should be concentrated.     

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.     

Relationships between vegetation indices,fractional cover retrievals and the structure and composition of Brazilian Cerrado natural vegetation

This study explores the use of the relationship between the normalized difference vegetation index (NDVI) and the shortwave infrared ratio (SWIR32) vegetation indices (VI) to retrieve fractional cover over the structurally complex natural vegetation of the Cerrado of Brazil using a time series of imagery from the Moderate Resolution Imaging Spectroradiometer (MODIS). Data from the EO-1 Hyperion sensor with 30 m pixel resolution is used to sample geographic and seasonal variation in NDVI, SWIR32, and the hyperspectral cellulose absorption index (CAI), and to derive end-member values for photosynthetic vegetation (PV), non-photosynthetic vegetation (NPV), and bare soil (BS) from a suite of protected and/or natural vegetation sites across the Cerrado. The end-members derived from relatively pure 30 m pixels are then applied to a 500 m pixel resolution MODIS time series using linear spectral unmixing to retrieve PV, NPV, and BS fractional cover (F_PV, F_NPV, and F_BS). The two-way interaction response of MODIS-equivalent NDVI and SWIR32 was examined for regions of interest (ROI) collected within protected areas and nearby converted lands. The MODIS NDVI, SWIR32 and retrieved F_PV, F_NPV, and F_BS are then compared to detailed cover and structural composition data from field sites, and the influence of the structural and compositional variation on the VIs and cover fractions is explored. The hyperion ROI analysis indicated that the two-way NDVI–SWIR32 response behaved as an effective surrogate for the two-way NDVI–CAI response for the campo limpo/grazed pasture to cerrado sensu stricto woody gradient. The SWIR32 sensitivity to the NPV and BS variation increased as the dry season progressed, but Cerrado savannah exhibited limited dynamic range in the NDVI–CAI and NDVI–SWIR32 two-way responses compared to the entire landscape, which also comprises fallow croplands and forests. Validation analysis of MODIS retrievals with Quickbird-2 images produced an RMSE value of 0.13 for F_PV. However, the RMSE values of 0.16 and 0.18 for F_BS and F_NPV, respectively, were large relative to the seasonal and inter-annual variation. Analysis of site composition and structural data in relation to the MODIS-derived NDVI, SWIR32 and F_PV, F_NPV, and F_BS, indicated that the VI signal and derived cover fractions were influenced by a complex mix of structure and cover but included a strong year-to-year seasonal effect. Therefore, although the MODIS NDVI–SWIR32 response could be used to retrieve cover fractions across all Cerrado land covers including bare cropland, pastures and forests, sensitivity may be limited within the natural Cerrado due to sub-pixel heterogeneity and limited BS and NPV sensitivity.     

Estimation of subpixel land surface temperature using an endmember index based technique: A case examination on ASTER and MODIS temperature products over a heterogeneous area

Land surface temperature (LST) is a key parameter in numerous environmental studies. Surface heterogeneity induces uncertainty in estimating subpixel temperature. To take an advantage of simultaneous, multi-resolution observations at coincident nadirs by the Advanced Spaceborne Thermal Emission Reflection Radiometer (ASTER) and the MODerate-resolution Imaging Spectroradiometer (MODIS), LST products from the two sensors were examined for a portion of suburb area in Beijing, China. We selected Soil-Adjusted Vegetation Index (SAVI), Normalized Multi-band Drought Index (NMDI), Normalized Difference Built-up Index (NDBI) and Normalized Difference Water Index (NDWI) as representative remote sensing indices for four land cover types (vegetation, bare soil, impervious and water area), respectively. By using support vector machines, the overall classification accuracy of the four land cover types with inputs of the four remote sensing indices, extracted from ASTER visible near infrared (VNIR) bands and shortwave infrared (SWIR) bands, reached 97.66%, and Kappa coefficient was 0.9632. In order to lower the subpixel temperature estimation error caused by re-sampling of remote sensing data, a disaggregation method for subpixel temperature using the remote sensing endmember index based technique (DisEMI) was established in this study. Firstly, the area ratios and statistical information of endmember remote sensing indices were calculated from ASTER VNIR/SWIR data at 990 m and 90 m resolutions, respectively. Secondly, the relationship between the 990 m resolution MODIS LST and the corresponding input parameters (area ratios and endmember indices at the 990 m resolution) was trained by a genetic algorithm and self-organizing feature map artificial neural network (GA-SOFM-ANN). Finally, the trained models were employed to estimate the 90 m resolution subpixel temperature with inputs of area ratios and endmember indices at the 90 m resolution. ASTER LST product was used for verifying the estimated subpixel temperature, and the verified results indicate that the estimated temperature distribution was basically consistent with that of ASTER LST product. A better agreement was found between temperatures derived by our proposed method (DisEMI) and the ASTER 90 m data (R² = 0.709 and RMSE = 2.702 K).