141 research outputs found

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

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    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\u27s 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

    Soil Moisture Estimate Uncertainties from the Effect of Soil Texture on Dielectric Semiempirical Models

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    Soil texture has been shown to affect the dielectric behavior of soil over the entire frequency range. Three universally employed dielectric semiempirical models (SEMs), the Dobson model, the Wang–Schmugge model and the Mironov model, as well as a new improved SEM known as the soil semi-empirical mineralogy-related-to-water dielectric model (SSMDM), incorporate a significant soil texture effect in different ways. In this paper, soil moisture estimate uncertainties from the effect of soil texture on these four SEMs are systematically and widely investigated over all soil texture cases at different frequencies between 1.4 and 18 GHz for volumetric water content levels between 0.0 and 0.4 m3/m3 from the perspective of two aspects: soil dielectric model discordance and soil texture discordance. Firstly, the effect of soil texture on these four dielectric SEMs is analyzed. Then, soil moisture estimate uncertainties due to the effect of soil texture are carefully investigated. Finally, the applicability of these SEMs is discussed, which can supply references for their choice. The results show that soil moisture estimate uncertainties are small and satisfy the 4% volumetric water content retrieval requirement in some cases. However, in other cases, it may contribute relatively significant uncertainties to soil moisture estimates and correspond to a difference that exceeds the 4% volumetric water content requirement, with potential for the largest deviations to exceed 0.22 m3/m3

    Evaluation on Radiometric Capability of Chinese Optical Satellite Sensors

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    The radiometric capability of on-orbit sensors should be updated on time due to changes induced by space environmental factors and instrument aging. Some sensors, such as Moderate Resolution Imaging Spectroradiometer (MODIS), have onboard calibrators, which enable real-time calibration. However, most Chinese remote sensing satellite sensors lack onboard calibrators. Their radiometric calibrations have been updated once a year based on a vicarious calibration procedure, which has affected the applications of the data. Therefore, a full evaluation of the sensors’ radiometric capabilities is essential before quantitative applications can be made. In this study, a comprehensive procedure for evaluating the radiometric capability of several Chinese optical satellite sensors is proposed. In this procedure, long-term radiometric stability and radiometric accuracy are the two major indicators for radiometric evaluation. The radiometric temporal stability is analyzed by the tendency of long-term top-of-atmosphere (TOA) reflectance variation; the radiometric accuracy is determined by comparison with the TOA reflectance from MODIS after spectrally matching. Three Chinese sensors including the Charge-Coupled Device (CCD) camera onboard Huan Jing 1 satellite (HJ-1), as well as the Visible and Infrared Radiometer (VIRR) and Medium-Resolution Spectral Imager (MERSI) onboard the Feng Yun 3 satellite (FY-3) are evaluated in reflective bands based on this procedure. The results are reasonable, and thus can provide reliable reference for the sensors’ application, and as such will promote the development of Chinese satellite data

    An Improved Aerosol Optical Depth Retrieval Algorithm for Moderate to High Spatial Resolution Optical Remotely Sensed Imagery

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    To extract quantitative land information accurately and monitor the air pollution at city scale from moderate to high spatial resolution (MHSR) with a resolution no coarser than 30 m, optical remotely sensed imagery and aerosol parameters, especially aerosol optical depth (AOD), are a necessary step. In this paper, we introduce a new algorithm that can effectively estimate the spatial distribution of atmospheric aerosols and retrieve surface reflectance from moderate to high spatial resolution imagery under general atmosphere and land surface conditions. This algorithm has been improved in the following three aspects: (i) it has been developed for most of the moderate to high spatial resolution remotely sensed imagery; (ii) it can be applied to all kinds of land surface types including bright surface; and (iii) it is completely automatic. This algorithm is therefore suitable for operational applications. The derived AOD in Beijing from Landsat Thematic Mapper (TM), Landsat Enhanced Thematic Mapper Plus (ETM+), and Huan Jing 1 (HJ-1/CCD) data is validated with AErosol Robotic NETwork (AERONET) ground measurements at Beijng and Xianghe stations

    Estimating global solar radiation using a hybrid parametric model from MODIS data over the Tibetan Plateau

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    The Tibetan Plateau plays an important role in global atmospheric circulation with its complex terrain. The downward surface short-wave radiation (DSSR) can be obtained from remote sensing data because of its sparse observations and rugged surface. In this paper, a satellite-based scheme is presented to retrieve all-sky downward surface shortwave radiation, which links a look-up table algorithm and satellite images. The look-up table for clear sky and cloudy sky was created separately using a comprehensive 1D physically based radiative transfer model SBDART to achieve a higher computational accuracy and efficiency compared to the comprehensive radiative transfer model. The estimated DSSR was validated using one year pyranometer measurements from 8 stations in the Tibetan Plateau and compared with GEWEX-SRB data with 10 spatial resolution. The result shows that the largest root mean square error was 60 W/m(2) (32%) at Guoluo station, and the least root mean square error was 13 W/m(2) (13%) at Golmud station. The bias was larger in summer and smaller in winter, which may be caused by uncertainties in the assumption from the ID radiative transfer model and MODIS cloud properties product for broken and inhomogeneous clouds. The algorithm we proposed can be applied globally without local calibration because it is independent of climate and the surface elevation. (C) 2014 Elsevier Ltd. All rights reserved.</p

    Radiometric Cross-Calibration of GF-4 in Multispectral Bands

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    The GaoFen-4 (GF-4), launched at the end of December 2015, is China’s first high-resolution geostationary optical satellite. A panchromatic and multispectral sensor (PMS) is onboard the GF-4 satellite. Unfortunately, the GF-4 has no onboard calibration assembly, so on-orbit radiometric calibration is required. Like the charge-coupled device (CCD) onboard HuanJing-1 (HJ) or the wide field of view sensor (WFV) onboard GaoFen-1 (GF-1), GF-4 also has a wide field of view, which provides challenges for cross-calibration with narrow field of view sensors, like the Landsat series. A new technique has been developed and used to calibrate HJ-1/CCD and GF-1/WFV, which is verified viable. The technique has three key steps: (1) calculate the surface using the bi-directional reflectance distribution function (BRDF) characterization of a site, taking advantage of its uniform surface material and natural topographic variation using Landsat Enhanced Thematic Mapper Plus (ETM+)/Operational Land Imager (OLI) imagery and digital elevation model (DEM) products; (2) calculate the radiance at the top-of-the atmosphere (TOA) with the simulated surface reflectance using the atmosphere radiant transfer model; and (3) fit the calibration coefficients with the TOA radiance and corresponding Digital Number (DN) values of the image. This study attempts to demonstrate the technique is also feasible to calibrate GF-4 multispectral bands. After fitting the calibration coefficients using the technique, extensive validation is conducted by cross-validation using the image pairs of GF-4/PMS and Landsat-8/OLI with similar transit times and close view zenith. The validation result indicates a higher accuracy and frequency than that given by the China Centre for Resources Satellite Data and Application (CRESDA) using vicarious calibration. The study shows that the new technique is also quite feasible for GF-4 multispectral bands as a routine long-term procedure