758 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

    Oblique Longwave Infrared Atmospheric Compensation

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    This research introduces two novel oblique longwave infrared atmospheric compensation techniques for hyperspectral imagery, Oblique In-Scene Atmospheric Compensation (OISAC) and Radiance Detrending (RD). Current atmospheric compensation algorithms have been developed for nadir-viewing geometries which assume that every pixel in the scene is affected by the atmosphere in nearly the same manner. However, this assumption is violated in oblique imaging conditions where the transmission and path radiance vary continuously as a function of object-sensor range, negatively impacting current algorithms in their ability to compensate for the atmosphere. The techniques presented here leverage the changing viewing conditions to improve rather than hinder atmospheric compensation performance. Initial analyses of both synthetic and measured hyperspectral images suggest improved performance in oblique viewing conditions compared to standard techniques. OISAC is an extension of ISAC, a technique that has been used extensively for LWIR AC applications for over 15 years, that has been developed to incorporate the range-dependence of atmospheric transmission and path radiance in identification of the atmospheric state. Similar to ISAC, OISAC requires the existence of near blackbody-like materials over the 11.73 micrometer water band. RD is a newer technique which features unsupervised classification of materials and identifies the atmospheric state which best detrends the observed radiance across all classes of materials, including those of low emissivity

    Estimation of Surface Moisture Content and Evapotranspiration Using Weightage Approach.

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    Soil moisture (MC) and evapotranspiration (ET) are considered as the most significant boundary conditions controlling most of the hydrological cycle’s processes. However, monitoring them continuously over large areas using the high temporal-resolution optical satellites is very demanding. Satellites such as the Advanced Very High Resolution Radiometer (AVHRR) and the Moderate Resolution Imaging Spectroradiometer (MODIS), have a coarse spatial resolution in their images. Thus it not only impedes the acquisition of an accurate MC and ET but also represents multispectral reflections from the holistic surface features. This beside their dependence on vegetation and ground coefficient when assessing MC and ET. The study aims to enhance the spatial accuracy by weighting the MC produced from different surface cover classes within the pixel. MC for each pixel is segmented into three (3) different classes namely urban, vegetation and multi surface cover according to their respective MC weightage. Secondly, to generate an improved actual ETa map by overlaying the segmented MC with a rectified ETo. Images from AVHRR and MODIS satellites were selected in order to generate MC and ET maps. Two powerful MC algorithms were used based on land Surface Temperature (Ts), vegetation Indices (VI) and field measurements of MC; which were conducted at variable depths to examine the depth influence on MC and Ts magnitudes

    Thermal infrared dust optical depth and coarse-mode effective diameter retrieved from collocated MODIS and CALIOP observations

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    In this study, we developed a novel algorithm based on the collocated Moderate Resolution Imaging Spectroradiometer (MODIS) thermal infrared (TIR) observations and dust vertical profiles from the Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) to simultaneously retrieve dust aerosol optical depth at 10 &micro;m (DAOD10&mu;m) and the coarse-mode dust effective diameter (Deff) over global oceans. The accuracy of the Deff retrieval is assessed by comparing retrieved Deff with the in-situ measured dust particle size distributions (PSDs) from the AER-D, SAMUM-2 and SALTRACE field campaigns through case studies. The new DAOD10&mu;m retrievals were evaluated first through comparisons with the collocated DAOD10.6&mu;m retrieved from the combined Imaging Infrared Radiometer (IIR) and CALIOP observations from our previous study (Zheng et al. 2022). The pixel-to-pixel comparison of the two retrievals indicates a good agreement (R~0.7) and a significant reduction of (~50 %) retrieval uncertainties largely thanks to the better constraint on dust size. In a climatological comparison, the seasonal and regional (5&deg;&times;2&deg;) mean DAOD10um retrievals based on our combined MODIS and CALIOP method are in good agreement with the two independent Infrared Atmospheric Sounding Interferometer (IASI) products over three dust transport regions (i.e., North Atlantic (NA; R = 0.9), Indian Ocean (IO; R = 0.8) and North Pacific (NP; R = 0.7)). Using the new retrievals from 2013 to 2017, we performed a climatological analysis of coarse mode dust Deff over global oceans. We found that dust Deff over IO and NP are up to 20 % smaller than that over NA. Over NA in summer, we found a ~50 % reduction of the number of retrievals with Deff &gt; 5 &mu;m from 15&deg; W to 35&deg; W and a stable trend of Deff average at 4.4 &mu;m from 35&deg; W throughout the Caribbean Sea (90&deg; W). Over NP in spring, only ~5 % of retrieved pixels with Deff &gt; 5 &mu;m are found from 150&deg; E to 180&deg;, while the mean Deff remains stable at 4.0 &mu;m throughout eastern NP. To our best knowledge, this study is the first to retrieve both DAOD and coarse-mode dust particle size over global oceans for multiple years. This retrieval dataset provides insightful information for evaluating dust long-wave radiative effects and coarse mode dust particle size in models.</p

    Harmonization of remote sensing land surface products : correction of clear-sky bias and characterization of directional effects

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    Tese de doutoramento, Ciências Geofísicas e da Geoinformação (Deteção Remota), Universidade de Lisboa, Faculdade de Ciências, 2018Land surface temperature (LST) is the mean radiative skin temperature of an area of land resulting from the mean energy balance at the surface. LST is an important climatological variable and a diagnostic parameter of land surface conditions, since it is the primary variable determining the upward thermal radiation and one of the main controllers of sensible and latent heat fluxes between the surface and the atmosphere. The reliable and long-term estimation of LST is therefore highly relevant for a wide range of applications, including, amongst others: (i) land surface model validation and monitoring; (ii) data assimilation; (iii) hydrological applications; and (iv) climate monitoring. Remote sensing constitutes the most effective method to observe LST over large areas and on a regular basis. Satellite LST products generally rely on measurements in the thermal infrared (IR) atmospheric window, i.e., within the 8-13 micrometer range. Beside the relatively weak atmospheric attenuation under clear sky conditions, this band includes the peak of the Earth’s spectral radiance, considering surface temperature of the order of 300K (leading to maximum emission at approximately 9.6 micrometer, according to Wien’s Displacement Law). The estimation of LST from remote sensing instruments operating in the IR is being routinely performed for nearly 3 decades. Nevertheless, there is still a long list of open issues, some of them to be addressed in this PhD thesis. First, the viewing position of the different remote sensing platforms may lead to variability of the retrieved surface temperature that depends on the surface heterogeneity of the pixel – dominant land cover, orography. This effect introduces significant discrepancies among LST estimations from different sensors, overlapping in space and time, that are not related to uncertainties in the methodologies or input data used. Furthermore, these directional effects deviate LST products from an ideally defined LST, which should correspond to the ensemble directional radiometric temperature of all surface elements within the FOV. In this thesis, a geometric model is presented that allows the upscaling of in situ measurements to the any viewing configuration. This model allowed generating a synthetic database of directional LST that was used consistently to evaluate different parametric models of directional LST. Ultimately, a methodology is proposed that allows the operational use of such parametric models to correct angular effects on the retrieved LST. Second, the use of infrared data limits the retrieval of LST to clear sky conditions, since clouds “close” the atmospheric window. This effect introduces a clear-sky bias in IR LST datasets that is difficult to quantify since it varies in space and time. In addition, the cloud clearing requirement severely limits the space-time sampling of IR measurements. Passive microwave (MW) measurements are much less affected by clouds than IR observations. LST estimates can in principle be derived from MW measurements, regardless of the cloud conditions. However, retrieving LST from MW and matching those estimations with IR-derived values is challenging and there have been only a few attempts so far. In this thesis, a methodology is presented to retrieve LST from passive MW observations. The MW LST dataset is examined comprehensively against in situ measurements and multiple IR LST products. Finally, the MW LST data is used to assess the spatial-temporal patterns of the clear-sky bias at global scale.Fundação para a Ciência e a Tecnologia, SFRH/BD/9646

    Remote Sensing Monitoring of Land Surface Temperature (LST)

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    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

    Validation of AVHRR Land Surface Temperature with MODIS and In Situ LST—A TIMELINE Thematic Processor

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    Land Surface Temperature (LST) is an important parameter for tracing the impact of changing climatic conditions on our environment. Describing the interface between long- and shortwave radiation fluxes, as well as between turbulent heat fluxes and the ground heat flux, LST plays a crucial role in the global heat balance. Satellite-derived LST is an indispensable tool for monitoring these changes consistently over large areas and for long time periods. Data from the AVHRR (Advanced Very High-Resolution Radiometer) sensors have been available since the early 1980s. In the TIMELINE project, LST is derived for the entire operating period of AVHRR sensors over Europe at a 1 km spatial resolution. In this study, we present the validation results for the TIMELINE AVHRR daytime LST. The validation approach consists of an assessment of the temporal consistency of the AVHRR LST time series, an inter-comparison between AVHRR LST and in situ LST, and a comparison of the AVHRR LST product with concurrent MODIS (Moderate Resolution Imaging Spectroradiometer) LST. The results indicate the successful derivation of stable LST time series from multi-decadal AVHRR data. The validation results were investigated regarding different LST, TCWV and VA, as well as land cover classes. The comparisons between the TIMELINE LST product and the reference datasets show seasonal and land cover-related patterns. The LST level was found to be the most determinative factor of the error. On average, an absolute deviation of the AVHRR LST by 1.83 K from in situ LST, as well as a difference of 2.34 K from the MODIS product, was observed

    Innovative Techniques for the Retrieval of Earth’s Surface and Atmosphere Geophysical Parameters: Spaceborne Infrared/Microwave Combined Analyses

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    With the advent of the first satellites for Earth Observation: Landsat-1 in July 1972 and ERS-1 in May 1991, the discipline of environmental remote sensing has become, over time, increasingly fundamental for the study of phenomena characterizing the planet Earth. The goal of environmental remote sensing is to perform detailed analyses and to monitor the temporal evolution of different physical phenomena, exploiting the mechanisms of interaction between the objects that are present in an observed scene and the electromagnetic radiation detected by sensors, placed at a distance from the scene, operating at different frequencies. The analyzed physical phenomena are those related to climate change, weather forecasts, global ocean circulation, greenhouse gas profiling, earthquakes, volcanic eruptions, soil subsidence, and the effects of rapid urbanization processes. Generally, remote sensing sensors are of two primary types: active and passive. Active sensors use their own source of electromagnetic radiation to illuminate and analyze an area of interest. An active sensor emits radiation in the direction of the area to be investigated and then detects and measures the radiation that is backscattered from the objects contained in that area. Passive sensors, on the other hand, detect natural electromagnetic radiation (e.g., from the Sun in the visible band and the Earth in the infrared and microwave bands) emitted or reflected by the object contained in the observed scene. The scientific community has dedicated many resources to developing techniques to estimate, study and analyze Earth’s geophysical parameters. These techniques differ for active and passive sensors because they depend strictly on the type of the measured physical quantity. In my P.h.D. work, inversion techniques for estimating Earth’s surface and atmosphere geophysical parameters will be addressed, emphasizing methods based on machine learning (ML). In particular, the study of cloud microphysics and the characterization of Earth’s surface changes phenomenon are the critical points of this work

    The retrieval of surface parameters from satellite borne infrared radiometers for the study of climate

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    This thesis concerns the development and application of new infrared remote sensing techniques for measurement of climate-related variables. The nature of the climate system is discussed, and the need for global monitoring is noted, together with the suitability of satellite-based remote sensing for the task. Current applications of data from satellite-borne infrared radiometers are discussed, together with the attendant problems, particularly that of correction for the effects of the atmosphere on remotely-sensed thermal infrared temperatures. In addition, the monitoring of proxy indicators of climatic change, such as the areas of closed lakes, by remote sensing is seen as having great potential, despite the limited research to date. The problem of accurate measurement of lake areas by the necessarily coarse resolution instruments which are capable of providing the required repeat coverage is addressed. An initial case study shows that lakes of order a few hundred km2 can be measured to an accuracy of 1% with 1 km resolution data from the Advanced Very High Resolution Radiometer (AVHRR). A further study of a climatically-sensitive closed lake in Ethiopia demonstrates a qualitative relationship between the measured area cycle and climate records. It is noted that the accurate remote sensing of lake surface temperatures and tropical ocean surface temperatures, both important parameters for climate research, is difficult due to the problem of atmospheric correction. A new correction algorithm is developed which offers an improvement of a factor ~2 over conventional algorithms when applied to AVHRR data. Useful byproducts of the algorithm are accurate atmospheric transmittance and total water vapour. Further developments of the techniques devised are suggested with a view to maximising the exploitation of both new and existing global datasets in order to provide the necessary long time series of accurate measurements required for climate research
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