Estimation of storage heat flux in an urban area using ASTER data

The urban heat island phenomenon occurs as a result of the mixed effects of anthropogenic heat discharge, increased use of artificial impervious surface materials, and decreased vegetation cover. These factors modify the heat balance at the land surface and eventually raise the atmospheric temperature. It is important to quantify the surface heat balance in order to estimate the contributions of these factors. The present authors propose the use of storage heat flux to represent the heat flux between the land surface and the inside of the canopy for the heat balance analysis based on satellite remote sensing data. Surface heat fluxes were estimated around the city of Nagoya, Japan using Terra ASTER data and meteorological data. Seasonal and day-night differences in heat balance were compared using ASTER data acquired in the daytime on July 10, 2000, and January 2, 2004 and in the nighttime on September 26, 2003. In the central business and commercial districts, the storage heat flux was higher than those in the surrounding residential areas. In particular, in winter, the storage heat flux in the central urban area was 240 to 290 W m^− 2, which was much larger than the storage heat fluxes in residential areas, which ranged from 180 to 220 W m^− 2. Moreover, the negative storage heat flux in the central urban area was greater at night. This tendency implies that the urban surface stores heat during the daytime and discharges it at night. Extremely large negative storage heat flux occurred primarily in the industrial areas for both daytime and nighttime as a result of the enormous energy consumption by factories.     

Evaluation of ASTER and MODIS land surface temperature and emissivity products using long-term surface longwave radiation observations at SURFRAD sites

Land surface temperature (LST) and emissivity are key parameters in estimating the land surface radiation budget, a major controlling factor of global climate and environmental change. In this study, Terra Advanced Spaceborne Thermal Emission Reflection Radiometer (ASTER) and Aqua MODerate resolution Imaging Spectroradiometer (MODIS) Collection 5 LST and emissivity products are evaluated using long-term ground-based longwave radiation observations collected at six Surface Radiation Budget Network (SURFRAD) sites from 2000 to 2007. LSTs at a spatial resolution of 90 m from 197 ASTER images during 2000-2007 are directly compared to ground observations at the six SURFRAD sites. For nighttime data, ASTER LST has an average bias of 0.1 °C and the average bias is 0.3 °C during daytime. Aqua MODIS LST at 1 km resolution during nighttime retrieved from a split-window algorithm is evaluated from 2002 to 2007. MODIS LST has an average bias of − 0.2 °C. LST heterogeneity (defined as the Standard Deviation, STD, of ASTER LSTs in 1 × 1 km² region, 11 × 11 pixel in total) and instrument calibration error of pyrgeometer are key factors impacting the ASTER and MODIS LST evaluation using ground-based radiation measurements. The heterogeneity of nighttime ASTER LST is 1.2 °C, which accounts for 71% of the STD of the comparison, while the heterogeneity of the daytime LST is 2.4 °C, which accounts for 60% of the STD. Collection 5 broadband emissivity is 0.01 larger than that of MODIS Collection 4 products and ASTER emissivity. It is essential to filter out the abnormal low values of ASTER daily emissivity data in summer time before its application.     

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).     

Remote sensing of the colour and temperature of volcanic lakes

Crater lakes on active volcanoes act as heat and chemical traps, and are amenable to surveillance from space. By use of all seven spectral bands, the Landsat Thematic Mapper (TM) can simultaneously measure: (i) lake surface temperature, (ii) lake surface area, and (iii) lake colour which is related to water chemistry. Use of the total instrument in this way enhances its utility for volcano surveillance. This work examines TM data for crater lakes at the following volcanoes: Ruapehu (New Zealand), Taal (Philippines), Kawah Ijen and Kelut (Indonesia), Poas (Costa Rica), and Apoyeque and Jiloa (Nicaragua). Observatory data indicate that lake surface temperatures derived by TM band 6 are typically 1-4°C less than contemporaneously measured bulk temperatures, probably due to the skin effect, the difference between water bulk and surface temperature. For Ruapehu, TM band 6 hotspots coincide approximately with known upwelling sites above volcanic vents on the lakebed. Field observations at Kawah Ijen show that the skin effect (< 3°C) is strongly correlated with windspeed: wind gusts peaking at ≈ 5m s^?1 caused rapid decreases in surface temperature of ≈ 0.5-1.0°C. These fluctuations are small compared with the magnitude of volcanogenic changes in lake temperature and do not reduce the utility of infrared surveillance. TM-derived water surface spectral reflectances indicate high concentrations of suspended chemical sediment in the most active crater lakes: Ruapehu, Poás and Ijen. For Ruapehu, imaged on two dates, the later scene reveals an upwelling slick, bright in bands 5 and 7, possibly composed of hollow sulphur spherules emitted from a subaqueous vent. The Advanced Spaceborne Thermal Emission and Reflectance Radiometer (ASTER) and Enhanced Thematic Mapper (ETM +) both due for launch in 1998, will offer improved capabilities for remote surveillance of crater lakes.     

Retrieval of high-resolution sea surface temperature data for Sendai Bay, Japan, using the Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER)

We studied sea surface temperature (SST) retrieval algorithms for Sendai Bay, using output from the thermal-infrared channels of the Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) on board Terra. While the highest resolutions of other satellite SST products are about 1 km, the ASTER thermal-infrared channels provide 90-m spatial resolution. To develop the ASTER algorithm, we employed statistical methods in which SSTs retrieved from the thermal-infrared measurements were tuned against the Moderate Resolution Imaging Spectroradiometer (MODIS) SST product with a 1-km spatial resolution. Terra also carries a MODIS sensor, which observed the same area as the ASTER sensor at the same time. The MODIS SST was validated around Sendai Bay, revealing a bias of −0.15 °C and root mean-square difference (RMSD) of 0.67 °C against in situ SSTs. Taking into account the spatial-resolution difference between ASTER and MODIS, match-up was generated only if the variability of ASTER brightness temperatures (T₁₃) was small in a pixel of MODIS SST (MP). The T₁₃ within one MP was about 121 pixels. The standard deviation (σ₁₃) of T₁₃ was calculated for each cloud-free MP, and the threshold of σ₁₃ for choosing match-up MPs was decided by analyzing the σ₁₃ histogram of one ASTER image. The 15 synchronous pairs of ASTER/MODIS images are separated into two groups of 8 pairs called set (A) and 7 pairs called set (B). Using the common procedure, the match-ups are generated for set (A) and set (B). The former is used for developing the ASTER Multi-Channel SST (MCSST) algorithm, and the latter for validation of the developed ASTER SST. Analysis of the whole 15 pairs indicated that ASTER SST does not depend on the satellite zenith angle. We concluded that, using Akaike's information criterion with set (A) match-ups, the multiple regression formula with all five thermal-infrared channels was adequate for the ASTER SST retrieval. Validation of ASTER SST using match-up set (B) indicated a bias of 0.101 °C and RMSD of 0.455 °C.     

Combining the triangle method with thermal inertia to estimate regional evapotranspiration — Applied to MSG-SEVIRI data in the Senegal River basin

Spatially distributed estimates of evaporative fraction and actual evapotranspiration are pursued using a simple remote sensing technique based on a remotely sensed vegetation index (NDVI) and diurnal changes in land surface temperature. The technique, known as the triangle method, is improved by utilizing the high temporal resolution of the geostationary MSG-SEVIRI sensor. With 15 min acquisition intervals, the MSG-SEVIRI data allow for a precise estimation of the morning rise in land surface temperature which is a strong proxy for total daytime sensible heat fluxes. Combining the diurnal change in surface temperature, dT_s with an interpretation of the triangular shaped dT_s − NDVI space allows for a direct estimation of evaporative fraction. The mean daytime energy available for evapotranspiration (R_n − G) is estimated using several remote sensors and limited ancillary data. Finally regional estimates of actual evapotranspiration are made by combining evaporative fraction and available energy estimates. The estimated evaporative fraction (EF) and actual evapotranspiration (ET) for the Senegal River basin have been validated against field observations for the rainy season 2005. The validation results showed low biases and RMSE and R² of 0.13 [−] and 0.63 for EF and RMSE of 41.45 W m^− 2 and R² of 0.66 for ET.     

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.     

Estimation of diurnal air temperature using MSG SEVIRI data in West Africa

Spatially distributed air temperature data with high temporal resolution are desired for several modeling applications. By exploiting the thermal split window channels in combination with the red and near infrared channels of the geostationary MSG SEVIRI sensor, multiple daily air temperature estimates can be achieved using the contextual temperature-vegetation index method. Air temperature was estimated for 436 image acquisitions during the 2005 rainy season over West Africa and evaluated against in situ data from a field test site in Dahra, Northern Senegal. The methodology was adjusted using data from the test site resulting in RMSE = 2.55 K, MBE = − 0.30 K and R² = 0.63 for the estimated versus observed air temperatures. A spatial validation of the method using 12 synoptic weather stations from Senegal and Mali within the Senegal River basin resulted in overall values of RMSE = 2.96 K, MBE = − 1.11 K and R² = 0.68. The daytime temperature curve is interpolated using a sine function based on the multiple daily air temperature estimates from the SEVIRI data. These estimates (covering the 8:00-20:00 UCT time window) were in good agreement with observed values with RMSE = 2.99 K, MBE = − 0.70 K and R² = 0.64. The temperature-vegetation index method was applied as a moving window technique to produce distributed maps of air temperature with 15 min intervals and 3 km spatial resolution for application in a distributed hydrological model.     

Capturing tree crown formation through implicit surface reconstruction using airborne lidar data

Forest structure data derived from lidar is being used in forest science and management for inventory analysis, biomass estimation, and wildlife habitat analysis. Regression analysis dominated previous approaches to the derivation of tree stem and crown parameters from lidar. The regression model for tree parameters is locally applied based on vertical lidar point density, the tree species involved, and stand structure in the specific research area. The results of this approach, therefore, are location-specific, limiting its applicability to other areas. For a more widely applicable approach to derive tree parameters, we developed an innovative method called ‘wrapped surface reconstruction’ that employs radial basis functions and an isosurface. Utilizing computer graphics, we capture the exact shape of an irregular tree crown of various tree species based on the lidar point cloud and visualize their exact crown formation in three-dimensional space. To validate the tree parameters given by our wrapped surface approach, survey-grade equipment (a total station) was used to measure the crown shape. Four vantage points were established for each of 55 trees to capture whole-tree crown profiles georeferenced with post-processed differential GPS points. The observed tree profiles were linearly interpolated to estimate crown volume. These fieldwork-generated profiles were compared with the wrapped surface to assess goodness of fit. For coniferous trees, the following tree crown parameters derived by the wrapped surface method were highly correlated (p < 0.05) with the total station-derived measurements: tree height (R² = 0.95), crown width (R² = 0.80), live crown base (R² = 0.92), height of the lowest branch (R² = 0.72), and crown volume (R² = 0.84). For deciduous trees, wrapped surface-derived parameters of tree height (R² = 0.96), crown width (R² = 0.75), live crown base (R² = 0.53), height of the lowest branch (R² = 0.51), and crown volume (R² = 0.89) were correlated with the total station-derived measurements. The wrapped surface technique is less susceptible to errors in estimation of tree parameters because of exact interpolation using the radial basis functions. The effect of diminished energy return causes the low correlation for lowest branches in deciduous trees (R² = 0.51), even though leaf-off lidar data was used. The wrapped surface provides fast and automated detection of micro-scale tree parameters for specific applications in areas such as tree physiology, fire modeling, and forest inventory.     

Monitoring yield and fruit quality parameters in open-canopy tree crops under water stress. Implications for ASTER

Work on water stress detection at tree and orchard levels using a high-spatial airborne thermal sensor is presented, showing its connection with yield and some fruit quality indicators in olive and peach commercial orchards under different irrigation regimes. Two airborne campaigns were conducted with the Airborne Hyperspectral Scanner (AHS) over olive and peach orchards located in Córdoba, southern Spain. The AHS sensor was flown at three different times on 25 July 2004 and 16 July 2005, collecting 2 m spatial resolution imagery in 80 spectral bands in the 0.43-12.5 μm spectral range. Thermal bands were assessed for the retrieval of land surface temperature using the split-window algorithm and TES (Temperature-Emissivity-Separation) method, separating pure crowns from shadows and sunlit soil pixels using the reflectance bands. Stem water potential and stomatal conductance were measured on selected trees at the time of airborne flights over the orchards. Tree fruit yield and quality parameters such as oil, weight and water content (for the olive trees), and fruit volume and weight (for the peach trees) were obtained at harvest and through laboratory analysis. Relationships between airborne-estimated crown temperature minus air temperature and stem water potential yielded r² = 0.5 (12:30 GMT) at the olive tree level, and r² = 0.81 (9:00 GMT) at the treatment level in peach trees. These results demonstrate that water stress can be detected at the crown level even under the usual water management conditions of commercial orchards. Regressions of yield and fruit quality against remote sensing estimates of crown temperature as an indicator of water stress, yielded r² = 0.95 (olive fruit water content) and r² = 0.94 (peach fruit mean diameter). These results suggest that high-spatial remote sensing thermal imagery has potential as an indicator of some fruit quality parameters for crop field segmentation and irrigation management purposes. A simulation study using ASTER spectral bands and aggregated pixels for stress detection as a function of irrigation level was conducted in order to study the applicability of medium resolution thermal sensors for the global monitoring of open-canopy tree crops. The determination coefficients obtained between the ASTER-simulated canopy temperature minus air temperature and stem water potential yielded r² = 0.58 (12:30 GMT) for olive trees, suggesting the potential for extrapolating these methods to ASTER satellite for global monitoring of open tree canopies.     

Development of a winter snow water equivalent algorithm using in situ passive microwave radiometry over snow-covered first-year sea ice

A snow water equivalent (SWE) algorithm has been developed for thin and thick snow using both in situ microwave measurements and snow thermophysical properties, collected over landfast snow covered first-year sea ice during the Canadian Arctic Shelf Exchange Study (CASES) overwintering mission from December 2003 to May 2004. Results showed that the behavior of brightness temperatures (T_bs) in thin snow covers was very different from those in a thick snowpack. Microwave SWE retrievals using the combination of T_b 19 GHz and air temperature (multiple regression) over thick snow are quite accurate, and showed very good agreement with the physical data (R² = 0.94) especially during the cooling period (i.e., from freeze up to the minimum air temperature recorded) where the snow is dry and cold. Thin snow SWE predictions also showed fairly good agreement with field data (R² = 0.70) during the cold season. The differences between retrieved and in situ SWE for both thin and thick snow cover are mainly attributable to the variations in air temperature, snow wetness and spatial heterogeneity in snow thickness.     

Utilizing SAR imagery and aquatic vegetation to map fresh and brackish lakes in the Brazilian Pantanal wetland

South America's Pantanal, the world's largest tropical wetland, contains hundreds of thousands of geochemically diverse lakes, ranging from dilute to brackish to saline in composition. These lakes form the backbone of the habitat that supports the highly diverse flora and fauna of the Pantanal, yet the natural processes that create and destroy them are largely unknown. The quantities and types of lakes in the Pantanal and their spatial distribution are therefore essential, but missing information required to understand the dynamics of the Pantanal ecosystem.RADARSAT S1 and S7, and JERS-1 imagery were integrated with field measurements of water geochemistry and characteristics of emergent aquatic vegetation for fresh and brackish lakes of the Nhecolândia region of the Brazilian Pantanal. A supervised classification was used to classify forest, pasture, bare soil, and lakes. A mask is then applied to produce an image of only lakes. The radar backscattering values were found to have a strong relationship with the emergent aquatic plant assemblages of the lakes—S1 imagery was the most useful. The plant assemblages, in turn, were observed to be strongly controlled by the total dissolved solids (TDS) and pH of the lakes. The relationships between backscattering values, plant assemblages, and geochemistry were then exploited to map the type and distribution of the lakes in the study area.Threshold rules were used to perform Level 1 and Level 2 classifications of the lakes. For the Level 1 classification, the σ^o values of RADARSAT S1 effectively separated brackish (10,000 > TDS > 1000 mg/kg) from fresh water lakes (TDS < 1000 mg/kg) with a total accuracy of 91%. For the Level 2 classification, the σ^o values of RADARSAT S1 effectively separated lakes into three geochemical groups: brackish (10,000 > TDS > 1000 mg/kg), hard with only Typha (1000 > TDS > 100 mg/kg), and fresh water lakes (TDS mg/kg < 100 mg/kg) with a total accuracy of 83%. Considering that the area is very remote and the lakes are very numerous, this may be the most feasible way to map lake type in the Pantanal.     

Active fire detection and characterization with the advanced spaceborne thermal emission and reflection radiometer (ASTER)

We present an automated fire detection algorithm for the Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) sensor capable of mapping actively burning fires at 30-m spatial resolution. For daytime scenes, our approach uses near infrared and short-wave infrared reflectance imagery. For nighttime scenes a simple short wave infrared radiance threshold is applied. Based on a statistical analysis of 100 ASTER scenes, we established omission and commission error rates for nine different regions. In most regions the probability of detection was between 0.8 and 0.9. Probabilities of false alarm varied between 9 × 10^− 8 (India) and 2 × 10^− 5 (USA/Canada). In most cases, the majority of false fire pixels were linked to clusters of true fire pixels, suggesting that most false fire pixels occur along ambiguous fire boundaries. We next consider fire characterization, and formulate an empirical method for estimating fire radiative power (FRP), a measure of fire intensity, using three ASTER thermal infrared channels. We performed a preliminary evaluation of our retrieval approach using four prescribed fires which were active at the time of the Terra overpass for which limited ground-truth data were collected. Retrieved FRP was accurate to within 20%, with the exception of one fire partially obscured by heavy soot.     

Development of a tundra-specific snow water equivalent retrieval algorithm for satellite passive microwave data

Airborne and satellite brightness temperature (T_B) measurements were combined with intensive field observations of sub-Arctic tundra snow cover to develop the framework for a new tundra-specific passive microwave snow water equivalent (SWE) retrieval algorithm. The dense snowpack and high sub-grid lake fraction across the tundra mean that conventional brightness temperature difference approaches (such as the commonly used 37 GHz-19 GHz) are not appropriate across the sub-Arctic. Airborne radiometer measurements (with footprint dimensions of approximately 70 × 120 m) acquired across sub-Arctic Canada during three field campaigns during the 2008 winter season were utilized to illustrate a slope reversal in the 37 GHz T_B versus SWE relationship. Scattering by the tundra snowpack drives a negative relationship until a threshold SWE value is reached near 130 mm at which point emission from the snowpack creates a positive but noisier relationship between 37 GHz T_B and SWE.The change from snowpack scattering to emission was also evident in the temporal evolution of 37 GHz T_B observed from satellite measurements. AMSR-E brightness temperatures (2002/03-2006/07) consistently exhibited decreases through the winter before reaching a minimum in February or March, followed by an increase for weeks or months before melt. The cumulative absolute change (Σ|Δ37V|) in vertically polarized 37 GHz T_B was computed at both monthly and pentad intervals from a January 1 start date and compared to ground measured SWE from intensive and regional snow survey campaigns, and climate station observations. A greater (lower) cumulative change in |Δ37V| was significantly related to greater (lower) ground measured SWE (r² = 0.77 with monthly averages; r² = 0.67 with pentad averages). Σ|Δ37V| was only weakly correlated with lake fraction: monthly r² values calculated for January through April 2003-2007 were largely less than 0.2. These results indicate that this is a computationally straightforward and viable algorithmic framework for producing tundra-specific SWE datasets from the complete satellite passive microwave record (1979 to present).     

Estimating northern peatland CO2 exchange from MODIS time series data

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

Regional evaporation estimates from flux tower and MODIS satellite data

Two models were evaluated for their ability to estimate land surface evaporation at 16-day intervals using MODIS remote sensing data and surface meteorology as inputs. The first was the aerodynamic resistance-surface energy balance model, and the second was the Penman-Monteith (P-M) equation, where the required surface conductance is estimated from remotely-sensed leaf area index. The models were tested using 3 years of evaporation and meteorological measurements from two contrasting Australian ecosystems, a cool temperate, evergreen Eucalyptus forest and a wet/dry, tropical savanna. The aerodynamic resistance-surface energy balance approach failed because small errors in the radiative surface temperature translate into large errors in sensible heat, and hence into estimates of evaporation. The P-M model adequately estimated the magnitude and seasonal variation in evaporation in both ecosystems (RMSE = 27 W m^− 2, R² = 0.74), demonstrating the validity of the proposed surface conductance algorithm. This, and the ability to constrain evaporation estimates via the energy balance, demonstrates the superiority of the P-M equation over the surface temperature-based model. There was no degradation in the performance of the P-M model when gridded meteorological data at coarser spatial (0.05°) and temporal (daily) resolution were substituted for locally-measured inputs.The P-M approach was used to generate a monthly evaporation climatology for Australia from 2001 to 2004 to demonstrate the potential of this approach for monitoring land surface evaporation and constructing monthly water budgets from 1-km to continental spatial scales.     

Residual errors in ASTER temperature and emissivity standard products AST08 and AST05

Land surface temperature and emissivity? are independent variables, and the thermal-infrared spectral radiance measured in remote sensing is dependent on both. Therefore the inverse Planck equation is under-determined, with two unknowns and a single measurement. Practical inversion algorithms designed to calculate temperature and emissivity from the measurements cannot do a perfect job of separation, and recovered temperature and emissivity may co-vary. For ASTER images, validation studies of recovered temperature and emissivity, regarded individually, have shown that they are within the precision and accuracy limits predicted in designing the ASTER TES algorithm used to calculate the standard products AST05 and AST08. Nevertheless, a closer look at emissivity recovered for water targets shows that emissivity appears to vary, incorrectly, as a function of temperature. One cause of this is electronic striping; another is incomplete characterization of atmospheric temperature and humidity profiles used in compensation for atmospheric absorption and path radiance. The linkage varies from band to band, with the greatest emissivity effect of − 0.0003 K^− 1 for ASTER band 12 (9.1 μm) relative to band 13 (10.6 μm). Although this inaccuracy in emissivity is small, it can approach or exceed the inaccuracy prediction of ± 0.015 for the standard product when the entire gamut of terrestrial water and land temperatures is examined. Therefore, spatial filtering and upgrading the atmosphere compensation algorithm to use water-vapor scaling should be considered in making AST05 and AST08.     

Spatial coherence between remotely sensed ocean color data and vertical distribution of lidar backscattering in coastal stratified waters

Detection of sub-surface optical layers in marine waters has important applications in fisheries management, climate modeling, and decision-based systems related to military operations. Concurrent changes in the magnitude and spatial variability of remote sensing reflectance (R_rs) ratios and submerged scattering layers were investigated in coastal waters of the northern Gulf of Alaska during summer of 2002 based on high resolution and simultaneous passive (MicroSAS) and active (Fish Lidar Oceanic Experimental, FLOE) optical measurements. Principal Component Analysis revealed that the spatial variability of total lidar backscattering signal (S) between 2.1 and 20 m depth was weakly associated with changes in the inherent optical properties (IOPs) of surface waters. Also based on a 250-m footprint, the vertical attenuation of S was inversely related to the IOPs (Spearman Rank Correlation up to −0.43). Low (arithmetic average and standard deviation) and high (skewness and kurtosis) moments of R_rs(443)/R_rs(490) and R_rs(508)/R_rs(555) ratios were correlated with vertical changes in total lidar backscattering signal (S) at different locations. This suggests the use of sub-pixel ocean color statistics to infer the spatial distribution of sub-surface scattering layers in coastal waters characterized by stratified conditions, well defined S layers (i.e., magnitude of S maximum comparable to near surface values), and relatively high vertically integrated phytoplankton pigments in the euphotic zone (chlorophyll a concentration > 150 mg m^− 2).     

Assessing structural effects on PRI for stress detection in conifer forests

The retrieval of indicators of vegetation stress from remote sensing imagery is an important issue for the accurate assessment of forest decline. The Photochemical Reflectance Index (PRI) has been demonstrated as a physiological index sensitive to the epoxidation state of the xanthophyll cycle pigments and to photosynthetic efficiency, serving as a proxy for short-term changes in photosynthetic activity, stress condition, and pigment absorption, but highly affected by illumination conditions, viewing geometry and canopy structure. In this study, a diurnal airborne campaign was conducted over Pinus sylvestris and Pinus nigra forest areas with the Airborne Hyperspectral Scanner (AHS) to evaluate the effects of canopy structure on PRI when used as an indicator of stress in a conifer forest. The AHS airborne sensor was flown at two times (8:00 GMT and 12:00 GMT) over forest areas under varying field-measured stress levels, acquiring 2 m spatial resolution imagery in 80 spectral bands in the 0.43-12.5 μm spectral range. Five formulations of PRI (based on R₅₃₁ as a xanthophyll-sensitive spectral band) were calculated using different reference wavelengths, such as PRI₅₇₀ (reference band R_REF = R₅₇₀), and the PRI modifications PRI_m1 (R_REF = R₅₁₂), PRI_m2 (R_REF = R₆₀₀), PRI_m3 (R_REF = R₆₇₀), and PRI_m4 (R_REF = R₅₇₀, R₆₇₀), along with other structural indices such as NDVI, SR, OSAVI, MSAVI and MTVI2. In addition, thermal bands were used for the retrieval of the land surface temperature. A radiative transfer modeling method was conducted using the LIBERTY and INFORM models to assess the structural effects on the PRI formulations proposed, studying the sensitivity of PRI_m indices to detect stress levels while minimizing the effects caused by the conifer architecture. The PRI indices were related to stomatal conductance, xanthophyll epoxidation state (EPS) and crown temperature. The modeling analysis showed that the coefficient of variation (CV) for PRI was 50%, whereas the CV for PRIm1 (band R512 as a reference) was only 20%. Simulation and experimental results demonstrated that PRI_m1 (R_REF = R₅₁₂) was less sensitive than PRI (R_REF = R₅₇₀) to changes in Leaf Area Index (LAI) and tree densities. PRI₅₁₂ was demonstrated to be sensitive to EPS at both leaf (r² = 0.59) and canopy level (r² = 0.40), yielding superior performance than PRI₅₇₀ (r² = 0.21) at the canopy level. In addition, PRI₅₁₂ was significantly related to water stress indicators such as stomatal conductance (Gs; r² = 0.45) and water potential (Ψ; r² = 0.48), yielding better results than PRI₅₇₀ (Gs, r² = 0.21; Ψ, r² = 0.21) due to the structural effects found on the PRI₅₇₀ index at the canopy level.     

Modeling fire severity in black spruce stands in the Alaskan boreal forest using spectral and non-spectral geospatial data

Biomass burning in the Alaskan interior is already a major disturbance and source of carbon emissions, and is likely to increase in response to the warming and drying predicted for the future climate. In addition to quantifying changes to the spatial and temporal patterns of burned areas, observing variations in severity is the key to studying the impact of changes to the fire regime on carbon cycling, energy budgets, and post-fire succession. Remote sensing indices of fire severity have not consistently been well-correlated with in situ observations of important severity characteristics in Alaskan black spruce stands, including depth of burning of the surface organic layer. The incorporation of ancillary data such as in situ observations and GIS layers with spectral data from Landsat TM/ETM+ greatly improved efforts to map the reduction of the organic layer in burned black spruce stands. Using a regression tree approach, the R² of the organic layer depth reduction models was 0.60 and 0.55 (p < 0.01) for relative and absolute depth reduction, respectively. All of the independent variables used by the regression tree to estimate burn depth can be obtained independently of field observations. Implementation of a gradient boosting algorithm improved the R² to 0.80 and 0.79 (p < 0.01) for absolute and relative organic layer depth reduction, respectively. Independent variables used in the regression tree model of burn depth included topographic position, remote sensing indices related to soil and vegetation characteristics, timing of the fire event, and meteorological data. Post-fire organic layer depth characteristics are determined for a large (> 200,000 ha) fire to identify areas that are potentially vulnerable to a shift in post-fire succession. This application showed that 12% of this fire event experienced fire severe enough to support a change in post-fire succession. We conclude that non-parametric models and ancillary data are useful in the modeling of the surface organic layer fire depth. Because quantitative differences in post-fire surface characteristics do not directly influence spectral properties, these modeling techniques provide better information than the use of remote sensing data alone.