Multi-angle Algorithm Research Articles

MAGARA: a Multi-Angle Geostationary Aerosol Retrieval Algorithm

Abstract. For over 40 years, the Geostationary Operational Environmental Satellite (GOES) system has provided frequent snapshots of the Western Hemisphere. The advanced baseline imagers (ABIs) on the GOES-16, GOES-17, and GOES-18 platforms are the first GOES-series imagers that meet the precision requirements for high-quality, aerosol-related research. We present MAGARA, a Multi-Angle Geostationary Aerosol Retrieval Algorithm, that leverages multi-angle ABI imagery to exploit the differences in autocorrelation timescales between surface reflectance, aerosol type, and aerosol loading. MAGARA retrieves pixel-level (up to 1 km) aerosol loading and fine-mode fraction at up to the cadence of the measurements (10 min), fine- and coarse-mode aerosol particle properties at a daily cadence, and surface properties by combining the multi-angle radiances with robust surface characterization inherent to temporally tiled algorithms. We present three case studies, and because GOES-17 was not making observations for one case, we present this as a unique demonstration of the multi-angle algorithm using only a single ABI sensor. We also compare MAGARA retrievals of fine-mode (FM) aerosol optical depth (AOD), coarse-mode (CM) AOD, and single-scattering albedo (SSA) statistically, with coincident AErosol RObotic NETwork (AERONET) spectral deconvolution algorithm (SDA) and inversion retrievals for the same period, and against bias-corrected NOAA GOES-16 and GOES-17 retrieved 550 nm AOD. For MAGARA vs. coincident AERONET over-land 500 nm fine-mode fraction and AOD>0.3, MAE=0.031, RMSE=0.100, and r=0.902, indicating good sensitivity to fine-mode fraction over land, especially for smoky regions. For bias-corrected MAGARA vs. coincident AERONET spectral single-scattering albedo with MAGARA AOD>0.5 (n=116), MAE=0.010, RMSE=0.015, and the correlation is 0.87. MAGARA performs best in regions where surface reflectance varies over long timescales with minimal clouds. This represents a large portion of the western half of the United States, much of north-central Africa and the Middle East, some of central Asia, and much of Australia. For these regions, aerosol type and aerosol loading on timescales as short as 10 min could allow for novel research into aerosol–cloud interactions, improvements to air-quality modeling and forecasting, and tighter constraints on direct aerosol radiative forcing.

Retrieving Soil and Vegetation Temperatures From Dual-Angle and Multipixel Satellite Observations

Land surface component temperatures (LSCTs), i.e., the temperatures of soil and vegetation, are important parameters in many applications, such as estimating evapotranspiration and monitoring droughts. However, the multiangle algorithm is affected due to different spatial resolution between nadir and oblique views. Therefore, we propose a combined retrieval algorithm that uses dual-angle and multipixel observations together. The sea and land surface temperature radiometer onboard ESA's Sentinel-3 satellite allows for quasi-synchronous dual-angle observations, from which LSCTs can be retrieved using dual-angle and multipixel algorithms. The better performance of the combined algorithm is demonstrated using a sensitivity analysis based on a synthetic dataset. The spatial errors in the oblique view due to different spatial resolution can reach 4.5 K and have a large effect on the multiangle algorithm. The introduction of multipixel information in a window can reduce the effect of such spatial errors, and the retrieval results of LSCTs can be further improved by using multiangle information for a pixel. In the validation, the proposed combined algorithm performed better, with LSCT root mean squared errors of 3.09 K and 1.91 K for soil and vegetation at a grass site, respectively, and corresponding values of 3.71 K and 3.42 K at a sparse forest site, respectively. Considering that the temperature differences between components can reach 20 K, the results confirm that, in addition to a pixel-average LST, the combined retrieval algorithm can provide information on LSCTs. This article demonstrates the potential of utilizing additional information sources for better LSCT results, which makes the presented combined strategy a promising option for deriving large-scale LSCT products.

Research on Monitoring of Thermal Pollution based on High Spatial Resolution Image

Review the theories of SST retrieval based on Thermal Infrared remote sensing images and gives the definitions of Skin and Bulk temperature,as well,summarizes the basic algorithms of SST retrieval,including Single Channel Algorithm,Split-window Algorithm and Multi-angle Algorithm,especially,focus on the SST Retrieval Algorithms of Landsat TM/ETM+,CBERS-02 IRMSS,ASTER,HJ-1B IRS in detail,which have high resolution TIR channel,finally,comparing characters of various TIR channel aboard and analyses the effective factors of SST retrieval,listing the problems at present and prospecting the research of future.

Experimental measurements for studying angular and spectral variation of thermal infrared emissivity.

One condition for precise multiangle algorithms for estimating sea and land surface temperature with the data from the Advanced Along Track Scanning Radiometer is accurate knowledge of the angular variation of surface emissivity in the thermal IR spectrum region. Today there are very few measurements of this variation. The present study is conducted to provide angular emissivity measurements for five representative samples (water, clay, sand, loam, gravel). The measurements are made in one thermal IR broadband (8-13 microm) and three narrower bands (8.2-9.2, 10.3-11.3, and 11.5-12.5 microm) at angles of 0 degrees-60 degrees (at 5 degrees increments) to the surface normal. The results show a general decrease in emissivity with increasing viewing angles, with the 8.2-9.2-microm channel the most sensitive to this dependence and sand the sample showing the greatest variation.

Split-window and multi-angle methods of sea surface temperature determination: An analysis

A physical model of split-window and multi-angle algorithms for the retrieval of sea surface temperature has been obtained. It is shown that optical thickness in the thermal infrared window region is approximately represented by a separable function of wavelength and atmospheric variables which greatly simplify the radiative transfer model. In modelling the temperature retrieval algorithms we exploit the correlation that exists between the sea surface temperature and the average temperature of the atmosphere. It is shown that, to the extent this correlation is maintained, there is a simple relationship between surface temperature and the brightness temperatures measured in two split-window or multi-angle channels. The different atmospheric conditions which cause spread in this relationship are briefly discussed. It is shown that measurements at three optical thicknesses instead of two as in the case of the split-window method improve the SST retrieval accuracy since it can take care of non-equilibrium conditions such as inversions, surface instabilities, etc., prevailing in the atmosphere.

Angular variation of thermal infrared emissivity for some natural surfaces from experimental measurements

Multiangle algorithms for estimating sea and land surface temperature with Along-Track Scanning Radiometer data require a precise knowledge of the angular variation of surface emissivity in the thermal infrared. Currently, few measurements of this variation exist. Here an experimental investigation of the angular variation of the infrared emissivity in the thermal infrared (8-14-microm) band of some representative samples was made at angles of 0 degrees-65 degrees (at 5 degrees increments) to the surface normal. The results show a decrease of the emissivity with increasing viewing angle, with water showing the highest angular dependence (approximately 7% from 0 degrees to 65 degrees views). Clay, sand, slime, and gravel show variations of approximately 1-3% for the same range of views, whereas a homogeneous grass cover does not show angular dependence. Finally, we include an evaluation of the impact that these data can produce on the algorithms for determining land and sea surface temperature from double-angle views.

Multi-channel and multi-angle algorithms for estimating sea and land surface temperature with ATSR data

Abstract A study has been carried out using LOWTRAN-7 simulations of the Along-Track Scanning Radiometer (ATSR) data at 11 and 12 μm wavelengths to compare the merits of the multi-angle technique with those of the currently used multi-channel technique (split-window method) to retrieve both sea surface temperature (SST) and land surface temperature (LST). To this end a simple single-channel double-angle viewing model is presented, which relates actual surface temperature to the two brightness temperatures measured from space in the two views of interest (ATSR nadir and forward). Subsequently, statistical retrieval coefficients for the double angle and split-window techniques are derived via a regression analysis of the synthetic measurements. The results show that the double angle technique is capable of producing SSTs with a standard deviation of 0.23 deg K if the satellite data are error free and, furthermore, confirm the advantage of the double-viewing angle technique in comparison with the split-window technique for LST determination in homogeneous surfaces if the emissivity's spectral variation and the emissivity's angular variation, are of the same order of magnitude. Finally we present the preliminary results obtained using the proposed model from ATSR data over a semi-arid region of New South Wales, Australia provided by Prata, and over the Pacific Ocean provided by Barton.

Land surface temperature determination from satellites

Methods for determining land surface temperatures (LSTs) from infrared satellite measurements are described. These include theoretical studies which provide algorithms in which the emissivity and atmospheric effects appear explicitly. Experimentally derived algorithms are also presented. These have been determined for a variety of surfaces including bare soils, partially vegetated and fully vegetated surfaces by using in situ surface mounted temperature sensors. Data from both the Advanced Very High Resolution Radiometer (AVHRR) and the Along Track Scanning Radiometer (ATSR) are used to compare the in situ temperatures with satellite estimates. Single channel, dual channel, split-window and multi-angle algorithms are developed. An attempt has been made to indicate the limiting accuracy of satellite derived LSTs by choosing the simplest case of a flat soil surface with uniform land cover. For both the theoretically determined algorithms and the experimental algorithms, the limiting accuracy lies between 1 and 2 °C. A theoretically derived LST algorithm is proposed for continental Australia which makes use of climatological profiles of temperature and water vapour to correct the thermal data.