Remote Sensing Monitoring of Land Surface Temperature (LST)

This book is a collection of recent developments, methodologies, calibration and validation techniques, and applications of thermal remote sensing data and derived products from UAV-based, aerial, and satellite remote sensing. A set of 15 papers written by a total of 70 authors was selected for this book. The published papers cover a wide range of topics, which can be classified in five groups: algorithms, calibration and validation techniques, improvements in long-term consistency in satellite LST, downscaling of LST, and LST applications and land surface emissivity research

Measuring and modeling near-surface reflected and emitted radiation fluxes at the FIFE site

Information is presented pertaining to the measurement and estimation of reflected and emitted components of the radiation balance. Information is included about reflectance and transmittance of solar radiation from and through the leaves of some grass and forb prairie species, bidirectional reflectance from a prairie canopy is discussed and measured and estimated fluxes are described of incoming and outgoing longwave and shortwave radiation. Results of the study showed only very small differences in reflectances and transmittances for the adaxial and abaxial surfaces of grass species in the visible and infrared wavebands, but some differences in the infrared wavebands were noted for the forbs. Reflectance from the prairie canopy changed as a function of solar and view zenith angles in the solar principal plane with definite asymmetry about nadir. The surface temperature of prairie canopies was found to vary by as much as 5 C depending on view zenith and azimuth position and on the solar azimuth. Aerodynamic temperature calculated from measured sensible heat fluxes ranged from 0 to 3 C higher than nadir-viewed temperatures. Models were developed to estimate incoming and reflected shortwave radiation from data collected with a Barnes Modular Multiband Radiometer. Several algorithms for estimating incoming longwave radiation were evaluated and compared to actual measures of that parameter. Net radiation was calculated using the estimated components of the shortwave radiation streams, determined from the algorithms developed, and from the longwave radiation streams provided by the Brunt, modified Deacon, and the Stefan-Boltzmann models. Estimates of net radiation were compared to measured values and found to be within the measurement error of the net radiometers used in the study

Uncertainty assessment of surface net radiation derived from Landsat images

The net radiation flux available at the Earth's surface drives evapotranspiration, photosynthesis and other physical and biological processes. The only cost-effective way to capture its spatial and temporal variability at regional and global scales is remote sensing. However, the accuracy of net radiation derived from remote sensing data has been evaluated up to now over a limited number of in situ measurements and ecosystems. This study aims at evaluating estimates and uncertainties on net radiation derived from Landsat-7 images depending on reliability of the input surface variables albedo, emissivity and surface temperature. The later includes the reliability of remote sensing information (spectral reflectances and top of canopy brightness temperature) and shortwave and longwave incoming radiations. Primary information describing the surface is derived from remote sensing observations. Surface albedo is estimated from spectral reflectances using a narrow-to-broadband conversion method. Land surface temperature is retrieved from top of canopy brightness temperature by accounting for land surface emissivity and reflection of atmospheric radiation; and emissivity is estimated using a relationship with a vegetation index and a spectral database of soil and plant canopy properties in the study area. The net radiation uncertainty is assessed using comparison with ground measurements over the Crau–Camargue and lower Rhone valley regions in France. We found Root Mean Square Errors between retrievals and field measurements of 0.25–0.33 (14–19%) for albedo, ~ 1.7 K for surface temperature and ~ 20 W·m− 2 (5%) for net radiation. Results show a substantial underestimation of Landsat-7 albedo (up to 0.024), particularly for estimates retrieved using the middle infrared, which could be due to different sources: the calibration of field sensors, the correction of radiometric signals from Landsat-7 or the differences in spectral bands with the sensors for which the models where originally derived, or the atmospheric corrections. We report a global uncertainty in net radiation of 40–100 W·m− 2 equally distributed over the shortwave and longwave radiation, which varies spatially and temporally depending on the land use and the time of year. In situ measurements of incoming shortwave and longwave radiation contribute the most to uncertainty in net radiation (10–40 W·m− 2 and 20–30 W·m− 2, respectively), followed by uncertainties in albedo (< 25 W·m− 2) and surface temperature (~ 8 W·m− 2). For the latter, the main factors were the uncertainties in top of canopy reflectances (< 10 W·m− 2) and brightness temperature (5–7 W·m− 2). The generalization of these results to other sensors and study regions could be considered, except for the emissivity if prior knowledge on its characterization is not available

2008: Distributed energy balance modelling of South Cascade Glacier, Washington and assessment of model uncertainty

[1] We have developed a physically based, distributed surface energy balance model to simulate glacier mass balance under meteorological and climatological forcing. Here we apply the model to estimate summer ablation on South Cascade Glacier, Washington, for the 2004 and 2005 mass balance seasons. To arrive at optimal mass balance simulations, we investigate and quantify model uncertainty associated with selecting from a range of physical parameter values that are not commonly measured in glaciological mass balance field studies. We optimize the performance of the model by varying values for atmospheric transmissivity, the albedo of surrounding topography, precipitationelevation lapse rate, surface roughness for turbulent exchange of momentum, and snow albedo aging coefficient. Of these the snow aging parameter and precipitation lapse rates have the greatest influence on the modeled ablation. We examined model sensitivity to varying parameters by performing an additional 10 3 realizations with parameters randomly chosen over a ±5% range centered about the optimum values. The best fit suite of model parameters yielded a net balance of À1.69 ± 0.38 m water equivalent (WE) for the 2004 water year and À2.10 ± 0.30 m WE up to 11 September 2005. The 2004 result is within 3% of the measured value. These simulations account for 91% and 93% of the variance in measured ablation for the respective years

Recovering land surface temperature under cloudy skies considering the solar‐cloud‐satellite geometry: application to MODIS and Landsat‐8 data

Clouds play a significant role in the derivation of land surface temperature (LST) from optical remote sensing. The estimation of LST under cloudy sky conditions has been a great challenge for the community for a long time. In this study, a scheme for recovering the LST under cloudy skies is proposed by accounting for the solar‐cloud‐satellite geometry effect, through which the LSTs of shadowed and illuminated pixels covered by clouds in the image are estimated. The validation shows that the new scheme can work well and has reasonable LST accuracy with a root mean square error < 4.9 K and bias < 3.5 K. The application of the new method to the Moderate Resolution Imaging Spectroradiometer (MODIS) and Landsat‐8 data reveals that the LSTs under cloud layers can be reasonably recovered and that the fraction of valid LSTs in an image can be correspondingly improved. The method is not data specific; instead, it can be used in any optical remote sensing images as long as the proper input variables are provided. As an alternative approach to derive cloudy sky LSTs based only on optical remote sensing data, it gives some new ideas to the remote sensing community, especially in the fields of surface energy balance

Evaluating spectral radiances simulated by the HadGEM2 global climate model using longwave satellite measurements

A 'model-to-radiance' comparison of simulated brightness temperatures and radiances from the Hadley Centre Global Environmental Model 2 (HadGEM2-A) with longwave measurements from the High Resolution Infrared Radiation Sounder/4 (HIRS/4) and the Infrared Atmospheric Sounding Interfermeter (IASI) onboard the MetOp-A satellite is presented for all-sky and clear-sky global means. The fast Radiative Transfer model for TOVS 10 (RTTOV-10) is applied to HadGEM2 output to simulate observational-equivalent data. The results are compared with corresponding broadband analyses. A method is developed to extend hyperspectral IASI radiances to cover the whole outgoing terrestrial spectrum, in order to identify any compensating biases, and explore wavebands in the unobserved Far Infrared (FIR) region. For the all-sky HIRS analysis, the model overestimates brightness temperatures in the atmospheric window region with the greatest biases over areas associated with deep convective cloud. In contrast to many global climate models, much smaller clear-sky biases are found indicating that model clouds are the dominating source of error. Simulated values in upper atmospheric CO2 channels approximate observations better as a result of compensating cold biases at the poles and warm biases at lower latitudes, due to a poor representation of the Brewer Dobson circulation in the 38 level 'low-top' configuration of the model. Simulated all and clear-sky outgoing longwave radiation evaluated against the Clouds and the Earth's Radiant Energy System (CERES) and HIRS OLR products reveal good agreement, in part due to cancellation of positive and negative biases. Through physical arguments relating to the spectral energy balance within a cloud, it is suggested that broadband agreement could be the result of a balance between positive window biases and unseen negative biases originating from the water vapour rotational band in the FIR (not sampled by HIRS). Simple sensitivity tests show that dramatically altering existing cloud properties has little effect on the prominent window biases, however raising clouds a maximum of 5 atmospheric levels minimises the error in cloud contaminated channels, due to the introduction of spatially compensating errors. Sensitivities to the way ice clouds are parameterised in RTTOV-10 display a range of up to 2.5 K in window channels but absolute biases still exceed 3 K for all choices. Because of the lack of satellite based FIR observations due to a technological gap in the spectral region, an algorithm is created to 'fill in' the available data. Correlations between selected IASI channels and simulated unobserved wavelengths in the far infrared are used to estimate radiances between 25.25 - 644.75 cm-1 at 0.5 cm-1 intervals. The same method is used in the 2760 - 3000 cm-1 region. The spectrum is validated by comparing the Integrated Nadir Longwave Radiance (INLR) product (spanning the whole 25.25 - 3000 cm-1 range) with the corresponding broadband measurements from the Clouds and the Earth's Radiant Energy System (CERES) instrument on the Terra and Aqua satellites at simultaneous nadir overpasses, revealing mean differences of 0.3 Wm-2sr-1 (0.5% relative difference) lower for IASI relative to CERES and significantly lower biases in nighttime only scenes. Averaged global data over a single month produces mean differences of about 1 Wm-2sr-1 in both the all and the clear-sky (1.2% relative difference). The new high resolution spectrum is presented for global mean clear and total skies where the far infrared is shown to contribute 44% and 47% to the total OLR respectively, which is consistent with previous estimates. In terms of spectral cloud radiative forcing, the FIR contributes 19% and in some subtropical instances appears to be negative, results that would go un-observed with a traditional broadband analysis. The equivalent complete IASI OLR model product is simulated from GCM data using RTTOV-10. The same process of applying predictors to the satellite measurements is applied to the model simulated radiances, with appropriate modifications, to produce a directly comparable model product. Annual mean all-sky radiances are still greatly overestimated at all wavenumbers with a total radiance bias of 4.52 Wm-2 across the whole range. Compensating negative biases outside of the HIRS coverage that were hypothesised are absent, with the far infrared contributing to the overall bias rather than cancelling it. Equivalent clear-sky biases are much lower overall at 0.39 Wm-2, in part due to spectral and spatial cancellation of errors. A flux-to-flux comparison is enabled by estimating the spatial distribution of anisotropic factors, using collated HIRS OLR fluxes and IASI OLR radiances, which yields global mean model fluxes in excess of 12 Wm-2 higher than observations in the all-sky. The difference between this and the fluxes calculated using the climate model's broadband radiation code (Edward-Slingo) are around 10 Wm-2 which is outside the range of uncertainty in the method used to estimate the flux. However, it is discussed that tuning of the climate model's broadband code to known flux values is a required practice to ensure global energy budgets balance but can produce inaccurate parameterised variables. An equivalent analysis adjusting the ice cloud parametrisation to reflect the radiances that have the biggest differences to the original configuration selected showed a bias reduction of 4.5 Wm-2, which is still not enough to completely explain its size, suggesting the existence of residual cloud problems. Finally, it is suggested that the way forward in separating and constraining cloud errors, in both radiative transfer codes, is a rigorous process of testing them with observation cloud properties and reanalysis data as inputs

Clouds and the Earth's Radiant Energy System (CERES) algorithm theoretical basis document

The theoretical bases for the Release 1 algorithms that will be used to process satellite data for investigation of the Clouds and the Earth's Radiant Energy System (CERES) are described. The architecture for software implementation of the methodologies is outlined. Volume 1 provides both summarized and detailed overviews of the CERES Release 1 data analysis system. CERES will produce global top-of-the-atmosphere shortwave and longwave radiative fluxes at the top of the atmosphere, at the surface, and within the atmosphere by using the combination of a large variety of measurements and models. The CERES processing system includes radiance observations from CERES scanning radiometers, cloud properties derived from coincident satellite imaging radiometers, temperature and humidity fields from meteorological analysis models, and high-temporal-resolution geostationary satellite radiances to account for unobserved times. CERES will provide a continuation of the ERBE record and the lowest error climatology of consistent cloud properties and radiation fields. CERES will also substantially improve our knowledge of the Earth's surface radiation budget