最新刊期
卷 25 ， 期 8 ， 2021
-
摘要：Evapotranspiration (ET) links the water cycle and energy exchange in hydrosphere, atmosphere, and biosphere. From a global perspective, ET accounts for approximately 60% of the total land precipitation, and the latent heat accompanying ET accounts for approximately 80% of the total surface net radiation energy. With the development of eddy covariance technology, global long-term and continuous observed meteorological and flux data are publicly available online. In last decade, data-driven remotely sensed ET retrieval methods have achieved rapid development. In terms of data-driven remotely sensed retrieval ET, this paper reviews and summarizes the existing researches on empirical regression methods, machine learning methods, data fusion methods and their corresponding products, then points out the existing problems and deficiencies on the driven data, retrieval methods, and available products. To be specific, these problems include: (1) There are few data-driven remotely sensed ET products with high precision and high spatiotemporal resolution; (2) The spatial scale mismatch between satellite pixel and in situ measurements makes the data-driven remotely sensed ET estimates challenging; (3) The lack of physical mechanisms for the data-driven remotely sensed retrieval ET methods and the insufficient regional representativeness for observed data from hundreds of sites, the spatial application of the ET model is limited; (4) Several important driving factors of ET, such as land surface temperature and soil moisture, were not sufficiently considered in previous studies; (5) The energy balance at flux measurement sites that based on eddy covariance method is not closed with about 0.8 unclosed rate globally, whether carry out energy balance closure correction before modeling is still a controversy; (6) The partitioning between soil evaporation and vegetation transpiration is of great significance, but the data-driven remotely sensed models that could estimate soil evaporation and vegetation transpiration respectively were not well studied. In the era of big data, as a double-edged sword, data-driven approaches are not only opportunities but also challenges, and several suggestions for future studies are proposed at the end. To begin with, the data-driven remotely sensed retrieval ET methods with high spatiotemporal resolution should be proposed. The observed source area should be introduced into the model constructing to solve the mismatch between satellite pixel and the measurements so as to improve the estimated ET accuracy. In addition, some important information, such as land surface temperature and soil moisture, which has an important effect on ET process should be taken into consideration effectively. Although vegetation index could indicate the long-term change of ET, land surface temperature could better indicate its short-term change. At the same time, soil moisture deficit would produce water stress on ET. Effective consideration of land surface temperature and soil moisture may improve the estimation accuracy of ET. Last but not least, it's important to emphasize that data-driven empirical approaches will not replace process-driven physical models, but strongly supplement and enrich the ET estimation methods. The combination of process-driven physical models and data-driven empirical approaches should be strengthened in order to obtain more reliable and accurate ET estimation by remote sensing. One suggestion is that, in future studies, data-driven approaches should be used to estimate important variables that closely related to ET but unavailable directly from remote sensing satellite at present, then physical models could be used for ET estimation. So as to the two kinds of models can fully play their roles respectively, jointly promote the research level of remotely sensed retrieval ET.关键词：land surface evapotranspiration;data-driven;land surface temperature;empirical method;machine learning;data fusion;remote sensing products- 2000
- |
- 807
- |
- 11
发布时间：2021-08-30 -
摘要：Land Surface Temperature (LST) and Land Surface Emissivity (LSE) are the direct driving force of long wave radiation and latent heat flux exchange at the surface-atmosphere interface, which are also two important parameters on surface energy budget balance and water balance at regional or global scales, its temporal and spatial variations on surface-atmosphere interface have a wide range of applications on weather forecasting, climate change, water cycle, geological exploration, agriculture, forestry monitoring and the urban thermal environment research, and many other scientific fields. Remote sensing provides an effective approach to obtain LST and LSE at global scale rather than point measurements for its rather wider spatial coverage and temporal revisit convenience. The validation of quantitative retrieval on LST and LSE products is conducive to find the defects of remote sensing data processing or drawbacks on retrieval algorithms, as well as clarify the accuracy and uncertainty of the operational products, which is of great convenience for the application and popularity of these products. In this paper, firstly different definitions of LST and LSE are reviewed, and then the scientific connotation of LST and LSE which can be retrieved from thermal infrared remote sensing data and measured from in-situ experiments is explained. The theoretical background, such as radiative transfer theory and methods of LST and LSE retrieval from remote sensing data are then summarized and outlined. After that, the framework of LST and LSE validation are summarized systematically, the validation metrics of LST and LSE derived from remotes sensing data including accuracy, precision, uncertainty, completeness and stability is constructed. Based on the validation framework, the methods of ground validation for LST and LSE are introduced (including directly validation and indirectly comparison), and followed by the methods of ground measurements of LST or related auxiliary data. The method of scale conversion method from point level to pixel level for heterogeneous and non-isothermal surfaces are emphasized and analyzed, and the main error sources of LST validation and LSE validation are discussed, respectively. A summarization of the main sites or networks for the validation of LST and LSE is conducted and the spatial distribution and the main characteristics (such as heterogeneity, land cover) of the typical LST and LSE validation sites or networks are briefly analyzed. The current LST and LSE products derived from satellite remote sensing data, which utilize the abovementioned validation sites or networks are summarized to report their validation accuracy or uncertainty, and related development on validation of LST and LSE are reviewed. Finally, some problems of validation of LST and LSE are also presented, and then the future outlook and trends of validation are outlined and justified.关键词：thermal infrared remote sensing;land surface temperature;emissivity;ground validation;verification- 1687
- |
- 527
- |
- 9
发布时间：2021-08-30 -
摘要：Hyperspectral thermal infrared data contains abundant long-wave spectral information, which can reveal radiation changes caused by the land-atmosphere coupling process more precisely and reflect the unique diagnostic characteristics of the thermal infrared spectrum. At the same time, the hyperspectral characteristics can also provide more reasonable assumptions and constraints for the ill-posed inversion of the key thermal infrared characteristic parameters. Therefore, the hyperspectral thermal infrared remote sensing has important research value and application prospect. Since its birth, hyperspectral thermal infrared remote sensing technology has developed rapidly on the basis of absorbing multispectral thermal infrared remote sensing technology, and has become an important research direction and breakthrough point of thermal infrared remote sensing research. However, there are some problems in the current hyperspectral thermal infrared remote sensing, such as lack of available data, traditional processing methods, limited inversion accuracies, and difficult implementation of the applications. To further clarify the research progress and existing challenges of hyperspectral thermal infrared remote sensing, based on the in-depth analysis of related literature, this paper sorts out the development process and hot spots of hyperspectral thermal infrared research, introduces the main hyperspectral thermal infrared sensors at home and abroad, and analyzes the current situation and problems of the atmospheric correction of hyperspectral atmospheric data, the separation of surface temperature and emissivity, and the integrated inversion of the key characteristic parameters of the land and atmosphere. Finally, the application of the relevant typical industries is summarized, and the future development direction of hyperspectral thermal infrared is prospected, so as to provide reference and help for the future research of hyperspectral thermal infrared remote sensing.关键词：hyperspectral;thermal infrared;remote sensing retrieval;land surface temperature;emissivity;atmospheric profile- 2165
- |
- 1431
- |
- 11
发布时间：2021-08-30 -
摘要：Land Surface Temperature (LST) is a pivotal factor in the energy exchange procedure between the land surface and the atmosphere. It plays a critical role in various study fields, including regional and global climate change analysis, environment monitoring, evapotranspiration estimation, and geothermal anomaly exploration. How to accurately capture LST from satellites data is one of the international hot spots and frontier topics in the quantitative remote sensing of surface parameters, and numbers of algorithms and products have been developed since 1960s. Specially, due to the advantage of high-spatial resolution, temporal continuity, and data availability, Landsat thermal infrared (TIR) data is generally used for LST retrieval. Landsat sensors and related LST products are introduced in detail at this paper, involving in Landsat 4-5 TM, Landsat 7 ETM+, and Landsat 8 TIRS. By analyzing the abundant academic papers, this article reviews the related publications and citations from 2000 to 2020 about Landsat LST retrieval by dividing them into two parts: algorithm and application. Furthermore, this paper systematically describes the algorithms for LST retrieved from Landsat TIR data including the Radiative Transfer Equation (RTE)-based algorithm, the mono-window algorithm, the generalized single-channel algorithm, the practical single-channel algorithm, and the split-window algorithm. On this basis, this article introduces the methods to obtain relevant parameters of each algorithm including atmospheric parameters and land surface emissivity. Furthermore, the calculation of atmospheric parameters mainly depends on water vapor and air temperature near the surface and atmospheric profiles, which can be obtained in three ways including ground-based sounding data, satellite inversion and reanalysis data. The methods estimating land surface emissivity depend on surface classification and NDVI images. Additionally, the superiority of high-spatial resolution LST from Landsat products makes them often applied to urban heat island effect, disaster monitoring, the LST impact for land use and land cover, where the studies require high-precision satellite images to facilitate detailed topics. With the development of science and technology, high-resolution data makes current problems in LST retrieval more and more obvious. According to the analysis for academic papers in the past 20 years, the research on the algorithm and application of LST retrieval based on Landsat TIR data shows an overall upward trend, and the Landsat LST retrieval and application will continuously play the important role in the future. Therefore, the prospective research trend and directions are proposed for Landsat TIR data, and this paper pointes out 4 directions for subsequent studies, including LST retrieval at the complex terrain region, LST retrieval under the cloud cover, spatio-temporal fusion of multi-source data, and long-term serial LST products. Finally, this article indicates that the uncertainty of land surface emissivity, real complex land surface, and banding effect causing LST errors. Therefore, more scholars should pay attention to these problems and actively propose new methods to solve the current deficiency. Moreover, it is helpful to further understand the mechanism of LST retrieval from remote sensing, provide inspiration for the establishment of new methods for remote sensing retrieval of LST, and promote the research level of quantitative remote sensing of LST in China..关键词：Landsat;thermal infrared data;land surface temperature;land surface emissivity;atmospheric parameter- 2193
- |
- 1468
- |
- 29
发布时间：2021-08-30
Review
-
摘要：During the entire operating life of a spaceborne sensor, the sensor’s spectral capability would be affected by optical device displacement, mechanical vibration, space environmental rays, etc. As a means to determine the spectral performance parameters of infrared hyperspectral sensors, high precision on-orbit spectral calibration is an essential prerequisite for quantitative remote sensing retrieval and application. Therefore, a fast and efficient spectral calibration method for hyperspectral mid-infrared and thermal infrared sensors needs to be constructed.In this paper, we established a spectral calibration method to retrieve the array centroid and Full Width at Half Maximum (FWHM) simultaneously in the absence of surface measurements. The method is mainly based on the atmospheric absorption line’s characteristics of the on-orbit effective brightness temperature (without the influence of atmospheric upward radiance) and the atmospheric transmittance spectrum, meanwhile, the spectral performance parameters were calibrated using a cost function composed of multiple spectrum matching algorithms. Before spectrum matching, it is necessary to perform Normalized Optical Depth Derivative (NODD) and continuum removal on the spectrum data.The Atmospheric Infrared Sounder (AIRS) spectral calibration results shown that the calibration accuracy of centroid frequency is better than 0.0154 cm-1, besides, the shift of centroid and FWHM are within ±0.02 cm-1 and ±0.1 cm-1, respectively. In other words, the centroid frequency and FWHM fluctuate within the 0.2%—1.9% and 0.5%—12.0% mean value of an array declared FWHM.This study demonstrated the utility of this method which can determine the mid-infrared and thermal infrared array spectral status change with high accuracy and stability. At the same time, we analyzed the sensitivity of this calibration method to atmospheric upward radiance, surface type, and the number of sampling points. The results confirmed that the influence of surface types on calibration accuracy is tolerable. Moreover, when the upward atmospheric radiance is accurately eliminated and the number of spatial sampling points is greater than 20, the spectral calibration result is reliable.关键词：spectral calibration;centroid frequency;FWHM;spectral matching;stepwise refinement;AIRS- 792
- |
- 398
- |
- 1
发布时间：2021-08-30 -
摘要：Hyperspectral thermal infrared image simulation provides potential application in military, scientific, and commercial fields, such as target detection, atmospheric correction, Land Surface Temperature (LST) and emissivity (LSE) separation and validation, and future satellite sensor bandwidth/resolution setup and optimization. Therefore, thermal infrared image simulation is an effective means for quantitative remote sensing research. In this paper, we simulated the Top of Atmosphere (TOA) hyperspectral thermal infrared radiation data with a spectral resolution of 0.25 cm-1, ranging from 8—14 μm (714—1250 cm-1), using ASTER (Advanced Spaceborne Thermal Emission and Reflection Radiometer) land surface temperature (AST08), land surface emissivity (AST05) product and the Seebor V5.0 atmospheric profiles. The surface hyperspectral emissivity is obtained from ASTER multi-spectral emissivity data using the principal component regression, while the various atmospheric parameters are obtained using the hyperspectral atmospheric radiative transfer model 4A/OP (operational release for automatized atmospheric absorption atlas) with the Seebor V5.0 atmospheric profile. At-nadir observations are considered in the simulations in this study. Finally, based on the above-mentioned simulation method of thermal infrared hyperspectral data, and further considering the influence of instrument observation error and other factors, two atmospheric correction algorithms based on image information, namely AAC algorithm (Autonomous Atmospheric Compensation) and combined ISAC (In Scene Atmospheric Correction) and AAC algorithm, and Iterative Spectrally Smooth Temperature Emissivity Separation (ISSTES) algorithm were evaluated respectively. And we also analyze the sensitivity of these methods to different noise levels and other error sources. The results show that the ISSTES temperature and emissivity separation algorithm is reliable and feasible when the accuracy of the atmospheric profile is relatively high, but when instrument noises and errors in the atmospheric profile are considered, the accuracy of ISSTES algorithm decreases. In the absence of instrument noise, the accuracy of AAC and ISAC-AAC atmospheric correction methods is close and can obtain high-precision atmospheric parameters of atmospheric transmittance, atmospheric downward radiation and atmospheric upwelling radiation, which are required in the atmospheric radiative transfer equation. When 1 K instrument noise is added, the accuracy of ISAC-AAC is significantly higher than that of AAC. And under the same instrument noise, with the increase of water vapor content, the accuracy of AAC algorithm decreases rapidly, while the accuracy of ISAC-AAC does not decrease significantly. The above evaluation results are consistent with the previous evaluation results based on the measured data. In general, the thermal infrared image simulation method proposed in this paper is feasible and can provide effective data for evaluating different atmospheric correction algorithms and temperature and emissivity separation algorithms.关键词：remote sensing;hyperspectral thermal infrared;image simulation;land surface temperature;land surface emissivity;atmospheric correction- 799
- |
- 340
- |
- 0
发布时间：2021-08-30 -
摘要：In order to meet the needs of high-precision monitoring of surface temperature remote sensing in environmental protection, land, agriculture, meteorology, disaster reduction and other industries of our country, the spatial resolution of our country’s long-wave infrared optical remote sensors has been increased from kilometer scale to ten-meter scale in recent years. The demand for quantitative applications is also increasing gradually. High-precision radiation calibration is the key to ensuring the quantitative application of infrared data. The detector of long wave infrared camera is limited by the chip material, chip preparation process, readout circuit design and production capacity and other reasons. Although the long wave infrared detector is constantly improving and developing, there are still some problems in varying degrees, such as large dark current, non-uniform response, low response and low temperature response, which directly affect the performance and radiometric calibration accuracy of long wave infrared camera.This article analyzes the factors that affect the accuracy of radiation calibration through the entire link. Combining with the specific development process of a certain model task, this article analyzes the optimization methods of the main influencing factors, including the optimization of the on-board calibration scheme, the improvement of temperature controlling accuracy and calibrating accuracy of the on-board blackbody calibration, the improvement of the performance of the long-wave detector and the improvement of the temperature controlling accuracy of the long-wave detector on the focal plane to improve the performance and output stability of the camera. The radiation calibration accuracy and the response state of system calibration has been verified through the vacuum radiation calibration test of this model. The test results show that the response slope of the radiometric calibration equation of the long wave infrared camera is effectively improved (from better than 45 of GF-5 01 satellite to better than 125 of GF-5 02 satellite), the output stability of the camera system is improved (DN value fluctuation is reduced from 20-30DN of GF-5 01 satellite to 1-2 DN of GF-5 02 satellite), and the accuracy of radiometric calibration is improved (from 1 K@300 K of GF-5 01 satellite decreased to 0.8 K@300 K of GF-5 01 satellite）.It reaches a high level among the same type of domestic long-wave infrared space optical remote sensors with a spatial resolution of less than 100 m. The performance improvement method of long-wave infrared space optical remote sensor introduced in this article can provide reference for the development and radiation calibration of similar remote sensors in the future. The calibration test verification results provided in this article can also provide references for the in-orbit applications of similar infrared satellites.关键词：optical remote sensing;space optical remote sensor;long-wave infrared;vacuum radiation calibration test;absolute radiation calibration accuracy- 1021
- |
- 275
- |
- 2
发布时间：2021-08-30 -
摘要：TIR remote sensing systems mainly use remote sensing means to sense the thermal infrared radiation difference of ground objects, which can be used to identify ground objects and retrieve surface temperature parameters. TIR remote sensing detecting technology has been widely used in resource investigation, ecological environment monitoring, disaster assessment and military target detection and recognition because of its excellent working ability in bad weather and night. With the deepening of engineering application and scientific research, it is urgent to improve the thermal sensitivity of infrared remote sensors. In the design of TIR remote sensing system, for the application needs of detection capability, the target, background need to consider the three main factors of the system. Noise Equivalent Temperature Difference (NETD) is an important indicator of representing the temperature sensitivity of the remote sensing system. NETD could be affected by the optical system radiation on the TIR remote sensing system.The influence of NETD by optical system radiation could be analyzed by the method of the number of noise electron or by the method of D* of the detector. The noise of the remote sensor system consists of photon noise (scene radiation and the fluctuation of the main background radiation reaching the focal plane), detector assembly noise and circuit noise. Under the condition of fixed imaging spectrum, integral time, detector and video circuit parameters, the cryogenic optical system can reduce its own radiation, reduce the photon noise, and improve the temperature sensitivity of the system. This paper quantified the relationship between optical system radiation and optical path design, operating temperature and the temperature sensitivity of thermal infrared remote sensing camera. The simulation method uses the software TracePro to conduct light tracing, analyzes the irradiance distribution of the optical system and the optical machine structure at the respective working temperature, and finally accumulates all the components to obtain the total radiation amount on the detector. The working temperature of optical system will affect the temperature sensitivity of the system. When the optical system radiation drops to lower than 1/10 of the target signal radiation, it could be regarded as a background-limiting detecting system, where the optical system radiation impact on the temperature sensitivity of the thermal infrared remote sensor can be ignored. In the load design, when the detector type is fixed, the sensitivity of load detection can be improved by reducing the optical system temperature. In order to verify the influence of changing the temperature of the optical system on the temperature sensitivity of the thermal infrared remote sensor, an airborne infrared remote sensor was designed and developed. The working temperature of the optical lens of the infrared remote sensor was changed form 313 K to 293 K, and NETD was tested. With the decrease of optical system temperature, the temperature sensitivity is improved. So the performance improvement was verified by the NETD testing. With the limitation of atmospheric temperature of airborne thermal infrared remote sensor below the dew point, this test did not carry out the performance verification of the temperature below 293 K. NETD test of the remote sensor with the lower temperature work on the deep low temperature working in the vacuum tank will be done in future. The development of this study is important for the design and development of cryogenic optical TIR remote sensing systems.关键词：remote sensing;FIR remote sensing system;detection capability;performance testing;Noise Equivalent Temperature Difference （NETD）- 774
- |
- 181
- |
- 1
发布时间：2021-08-30
Platforms & Loads
-
摘要：The spectral emissivity of high-resolution hyperspectral thermal infrared data can be used for mineral identification, and is regarded as an effective complement to optical remote sensing for land surface object recognition. Thermal Airborne Hyperspectral Imager (TASI) has 32 bands in the wavelength range of 8—11.5 μm, and thus can provide useful information for the retrieval of land surface temperature and emissivity spectrum. Therefore, TASI has been widely used in the fields of land surface thermal emission parameters estimate and mineral identification. On basis of the TASI images collected on October 2018 in Fuyun of Xinjiang province, this paper first performed atmospheric correction on the TASI image using the reanalysis atmospheric profiles from National Centers for Environmental Prediction (NCEP) dataset and the MODTRAN package, and then developed a Temperature and Emissivity Separation (TES) method to synchronously retrieve temperature and spectral emissivity from the surface-leaving radiance after the atmospheric correction. Ground multiple-band emissivity from the radiometer CE312 was applied for the verification of retrieved emissivity result, indicating a high accuracy of the retrieval result from TASI image, with an emissivity error about 0.01 for bands with wavelength larger than 0.96 μm. Finally, the spectral emissivity retrieved from TASI image was used to illustrate the spatial distribution of the kaolinite in the study area. It is thought that the algorithms and application results in this paper can provide an important reference for the airborne hyperspectral thermal infrared sensor in the coming future.关键词：remote sensing;airborne thermal infrared hyperspectral data;land surface temperature;Thermal Airborne Hyperspectral Imager (TASI）;emissivity spectrum;mineral identification- 944
- |
- 266
- |
- 0
发布时间：2021-08-30 -
摘要：Land surface temperature is the basic variable of the global climate observation system. Since most of the surface targets are mixed pixels with three-dimensional structure, complex composition, and uneven temperature distribution, compared with the average temperature, the component temperature has a clearer physical meaning and application value which is of great significance for the earth-atmosphere interaction process and quantitative analysis of water cycle. Vegetation and soil are two ground objects with completely different thermal properties in the vegetation-soil system. Obtaining accurate vegetation/soil component temperature is considered a prerequisite for improving the surface energy balance model. To develop component temperature inversion technology, this study proposes a new component temperature inversion algorithm based on Sentinel-3 SLSTR data.Based on the CBT-P model and the CE-P model, this study constructed a vegetation-soil component temperature inversion framework, and analyzed the impact of the split window algorithm and LAI on the inversion error. 946 clear sky atmospheric profiles were selected and the split window algorithm was used to obtain the surface brightness temperature. Then, LAI retrieved from the VNIR bands is used together with the measured component emissivity to be presented into the CE-P model to calculate the emissivity matrix. Finally, build an inversion framework based on the CBT-P model, and input the surface radiant brightness temperature and emissivity matrix to invert the component temperature.The results are verified using the measured data from Xiaotangshan, Luohe and Saihanba that show a high inversion accuracy can be achieved. The site vegetation types in these three places are mainly wheat and grassland, which belong to sparse vegetation and ridge crops respectively. At 5 sites, the vegetation component temperature retrieval error is between 0.1—1.6 K, and the average absolute error is 1.1 K. The soil component temperature retrieval error is between 0.5—1.4 K, and the average absolute error is 0.8 K. The single-factor error analysis results show that when the CWV is low, the split-window algorithm proposed in this study can bring about 1 K error to the vegetation component temperature inversion and bring no higher than 2.5 K error to the soil component temperature inversion which has a highly reliable inversion result. However, the error increases significantly in areas with high CWV, and the scope of application is limited. In the LAI single factor error analysis, when the LAI is large, the LAI error has a greater impact on the soil component temperature. When the LAI is greater than 4, 20% of the LAI error will cause the soil temperature inversion error to be greater than 3 K; when the LAI is small, the LAI error has a greater impact on the vegetation temperature. When the LAI is less than 1, the vegetation temperature retrieval error caused by LAI is close to 1 K.The result proves the feasibility of the Sentinel-3 SLSTR dual-channel dual-angle land surface component temperature inversion algorithm proposed in this study in the typical sparse vegetation and the row crops. However, this algorithm also has some shortcomings. The framework still has significant defects in areas with high CWV, and it still needs further verification in dense and continuous vegetation areas. In addition, this algorithm is based on the assumption of binary temperature and cannot handle the situation where the surface brightness temperature of the sensor 0° image is less than the 55° image, which is not uncommon in the actual inversion process. The component temperature inversion framework proposed in this study has the potential for integrated retrieval of atmospheric parameters and surface component temperature, which is worthy of further improvement.关键词：remote sensing;vegetation canopy;component temperature;split-window algorithm;CE-P Model;CBT-P Model;Sentinel-3 SLSTR- 1085
- |
- 374
- |
- 0
发布时间：2021-08-30 -
摘要：The Two-Source Energy Balance (TSEB) model is often used in the estimation of surface evapotranspiration which is important for the water resources regulation and utilization especially in arid and semi-arid areas. Using the different model inputs, such as land surface temperature and surface component temperatures (soil and vegetation) which are the key boundary of the TSEB model can vary the model performance in the prediction of evapotranspiration. In this paper, the Land Surface Temperature (LST) at nadir angle and Land Surface Component Temperatures (LSCT) retrieved from dual-angle observations of Sentinel-3 were used to drive the TSEB model in Heihe River Basin. At each site, the ground measurements data including net radiation, soil heat flux, sensible and latent heat fluxes were measured by radiometer, heat-plates, eddy covariance, respectively, and with the representativeness ranges from meters to hundreds of meters. The energy balance closure was enforced in the EC system observations using the Bowen ratio approach. What’s more, the large aperture scintillometers were used to measure the sensible heat flux over several kilometers areas to partly solve the mismatch between the model’s output and ground measurement.Then, the outputs including net radiation, soil heat flux, sensible and latent heat fluxes are evaluated using the ground measurements from EC and LAS which have larger source areas over the grassland, cropland and riparian forest landcover types in the Heihe River Basin, respectively. The results showed that both models overestimate the net radiation and latent heat flux with values of mean bias range from 50 to 150 W/m2 and from 60 to 130 W/m2 when compared with the ground measurements. However, the model performances of the TSEB-PT and TSEB-2T varied over different landcover types. In order to further explore the overestimation in latent heat flux from the two models, we intercompared the spatial pattern of plant transpiration estimated by the two models along with the moderate-resolution imaging spectroradiometer (MODIS) leaf area index data and the canopy temperature separated by the two models. It informed that the difference of model separated component temperatures mainly lead to the difference outputs of latent heat fluxes between TSEB-PT model and TSEB-2T model. Additionally, the TSEB-PT model mainly overestimated the canopy transpiration especially over the areas with high vegetation fraction coverage in the upstream of Heihe River Basin. This may due to the canopy temperature separated by the TSEB-PT model have lower values when compared with the inputs of canopy temperature in the TSEB-2T model. The lower canopy temperature could lead to lower sensible heat flux values and result in higher latent heat flux which is calculated as a residual in the land surface energy balance equation.The results showed that the TSEB-PT model has a better performance in the areas with low vegetation fraction coverage such as alpine meadows and riparian forest land cover types. However, the TSEB-2T model performed better in the areas with high vegetation fraction coverage, such as farmland, forest and so on. What’s more, as the fraction of vegetation coverage increase, the advantages of the TSEB-2T model are more obvious. The research results can provide water resources managers with more accurate estimates of land surface water consume over difference ecosystem.关键词：land surface temperature (TSEB-PT);component temperature (TSEB-2T);surface flux;validation- 838
- |
- 212
- |
- 0
发布时间：2021-08-30 -
摘要：The component temperature encapsulates more physical meaning than Land Surface Temperature (LST) and better meets the requirements of estimating evapotranspiration, monitoring drought and other studies. The polar-orbit satellites can observe the entire globe with a high spatial resolution and a modest temporal resolution from 1980 to present, and therefore have more wide applications than geostationary satellites. For these reasons, the study focuses on the methodology for estimating vegetation and soil component temperatures from polar-orbit satellite data.To meet operational and accurate requirements, the study proposed to use multi-temporal and multi-pixel data to separate the vegetation and soil component temperature. Specifically, a well-studied Diurnal Temperature Cycles (DTC) model was applied to link the two observations on one day, and then the moving-window technology was used to add available observations for solving the retrieval model. In addition, a spatial weighting matrix was adopted to improve the limitation of using multi-pixel data.The proposed algorithm was implemented by using Moderate Resolution Imaging Spectroradiometer (MODIS) data, and was evaluated by using in-situ measurements on Skukuza site and high-resolution Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) data, respectively. In the case of the validation of field data, the separation accuracy of component temperatures is about 2 K, and RMSEs of daytime vegetation, nighttime vegetation, daytime soil, and nighttime soil are 2.3 K, 2.5 K, 1.5 K and 1.9 K, respectively. The better performance at daytime is resulted from the fact that DTC model cannot describe the temperature decrease at night well. Regarding with the validation of ASTER data, the separation accuracies of the vegetation and soil component are 1.4 K and 1.7 K, respectively. The vegetation component is slightly overestimated (bias = 0.3 K) while the soil component is slightly underestimated (bias = -0.7 K), which is because of the systematic error between MODIS LST and ASTER LST. Moreover, this study also analyzed the influence of different time groups. Firstly, the combinations of one daytime moment and one nighttime moment can provide same estimation with high accuracy while the performance of the combination of two daytime moments is worse. The result is expected because two daytime moments are close to the maximum temperature moment, and therefore more sensitivity to temperature variation. Secondly, the performance of the time group from two sensors or one sensor is basically same, indicating that the time group is not limited by the sensor.This study proposed an algorithm for separating vegetation and soil component temperatures from polar-orbit satellite land surface temperatures. The practical method need only two observations from single or different sensors, i.e., one in daytime and the other one in nighttime, which makes it available for almost all sensors. The validation of field data and high-resolution data indicated that the separation accuracy is about 2 K and the best up to 1.4 K. Considering its accuracy, operationality and robustness, the proposed method would be an effective tool for separating component temperatures.关键词：remote sensing;component temperature;polar-orbit satellite;land surface temperature;multi-temporal;multi-pixel- 679
- |
- 288
- |
- 0
发布时间：2021-08-30 -
摘要：The ignoring of the land surface thermal radiation directionality hampered the accuracy improvement of current land surface temperature products. It is urgent to develop a practical method to correct the angle effect for the products. More and more attention has been paid to the thermal infrared kernel-drive model because of its simplicity and accuracy. For natural surface, there are widely used eight kernel-driven models. Their fitting abilities over continuous and discrete canopies is well-known, however, no report discussed their performances over the important row-planted stage. The objective of this study is to assess the fitting abilities of all existing eight thermal infrared kernel-driven models over row-planted canopies based on airborne measured datasets.Two multi-angle directional anisotropy datasets over row-planted vineyards were obtained through the airborne observation in several flights. The experiment sites were located at Château Talbot, Médoc region, 30 km north of Bordeaux, France. All the measurements were combined and corrected from nadir temperature to derive the directional anisotropy at 1° steps for view zenith angle (0°—60°) and view azimuth angle (0°—360°). The multi-angle directional anisotropy values were used as input to estimate the kernel coefficients of two 3-parameter models within the traditional visible and near infrared framework (Ross-Li and LSF-Li), two 3-parameter models within the new thermal infrared framework (Vinnikov and RL), and four 4-parameter models within the new thermal infrared framework (Vinnikov-RL, Vinnikov-Chen, LSF-RL, LSF-Chen). Then, the forward simulated directional anisotropy values of all models were compared taking the airborne measured values as reference.Results show that all eight kernel-driven models cannot simulate the row-structure signatures (i.e., axisymmetric feature). They have an overall large RMSE about 2 K and a low R2 less than 0.7. In addition, the RMSE differences between the models are small. For the east-west canopy, RMSE is between 1.930 K and 2.153 K, R2 is between 0.616 and 0.691. For the south-north canopy, RMSE is between 2.005 K and 2.353 K, R2 is between 0.600 and 0.670. Therefore, developing a new specific kernel for row-structure to improve the fitting ability is demanded in the thermal infrared band currently.关键词：remote sensing;land surface temperature;thermal radiation directionality;kernel-driven model;row-planted canopy- 865
- |
- 364
- |
- 0
发布时间：2021-08-30 -
摘要：Land Surface Temperature (LST) represents integrated features of land atmosphere physical and dynamic processe, It is a key element in the fields of climate change, the land–atmosphere energy budget, and the global hydrological cycle, vegetation monitoring urban climate and environmental studies. Thermal infrared remote sensing is an important technique for monitoring LST. However, the Moderate Resolution Imaging Spectroradiometer (MODIS) data are severely contaminated by cloud cover, which limits the applications of LST products. In recent years, the development of machine learning algorithms provides a promising technique for the reconstruction of LST under clouds. However, the accuracy of the cloud cover pixel reconstruction method based on machine learning is directly related to the number and regional distribution of training samples. In order to quantitatively evaluate the impact of the number and regional distribution of training samples on the LST reconstruction accuracy, based on MODIS land products and Meteosat Second Generation (MSG) incident short-wave radiation products, the LST reconstruction model depending on random forest method to construct an LST linking model for LSTs and a range of influencing factors were fitted based on clear-sky observations, which was then applied to cloud-covered pixels to obtain an LST reconstruction, the proposed reconstruction model was applied to carry out the influence of different training samples on the reconstruction accuracy of LST. The results show that: (1) A visual comparison with daily LST observations from (MSG) incident short-wave radiation products indicated that the LSTs reconstructed using this method were representative of LST patterns resulting from the influence of key variables including solar radiation intensity, vegetation cover, and geographical factor. (2) The accuracy of LST reconstruction improves significantly with the increase of the amount of training sample data, and the reconstruction accuracy is also different in different seasons. When the amount of training data increases from 5% to 95%, there are seasonal differences between summer and autumn due to the differences in vegetation and solar radiation. The variation range of correlation coefficient and root mean square error in summer is larger than that in autumn. (3) The random sampling method has higher and stabler accuracy than the regional sampling method because of its spatial representativeness, which can reduce the root mean square error to less than 2.1 k and increase the correlation coefficient to more than 0.93. Even if the amount of data is small, the reconstruction accuracy with the random sampling method is relatively stable, the negative effect of the insufficient number of training samples on the reduction of reconstruction accuracy is weakened. (4) The training sets were divided according to different elevations and vegetation coverage ranges to reconstruct the LST, and the results showed that the reconstruction accuracy was better when the range of the training sets included the range of the reconstruction area, that is, when the training set contains enough data features, it has a satisfactory spatial representation. The research results show that the proposed reconstruction model has a strong potential to reconstruct LSTs under cloud-covered conditions, and can also accurately describe the spatial distributions of LST. It also can provide a reference for future machine learning methods to select appropriate training samples and reconstruct the LST with high accuracy.关键词：land surface temperature;Random Forest;reconstruction;training dataset;elevation;vegetation- 804
- |
- 446
- |
- 3
发布时间：2021-08-30 -
摘要：Land Surface Temperature (LST) is a key parameter in global climate change research. Remote sensing is a practical means of obtaining surface temperature at global and regional scales, However, the existing single sensor cannot provide LST data with high spatial and temporal resolution, which limits the wide application of LST data obtained by remote sensing. The present downscaling methods are difficult to generate seamless LST data with high spatial and temporal resolution, and the downscaling effect is easily affected by the effective time distribution of LST data with high spatial resolution.In this paper, a land surface temperature downscaling method based on Diurnal Temperature Cycle (DTC)model deviation coefficient calculation is proposed.The LST data from FY-4A, MODIS and Landsat 8 are used to generate the seamless LST data of 100 meters per hour under clear skies and cloudy conditions.The proposed method mainly consists of four parts : (1) the seamless FY-4A LST data are obtained by using DTC model and LST reconstruction method considering spatial and temporal characteristics. (2) Establish the DTC model of FY-4A LST data. (3) The MODIS dataset are extended and then combined with the enhanced spatiotemporal adaptive reflectance fusion model (ESTARFM) to generate LST data of 100 meters at multiple moments every day. (4) Calculate the deviation coefficient of DTC model to obtain seamless LST data with a resolution of 100 meters per hour.Compared with the observation data of three stations, the results showed that: (1) the proposed method in this paper had higher accuracy compared with the ESTARFM model and its average RMSE had been reduced 0.63 K. MAE of the three stations was all less than 3 K, RMSE range was 2.01 K to 3.22 K, and the correlation coefficients r were all higher than 0.98. (2) the proposed method to extend LST data with medium spatial resolution was simple and effective. Compared with the MODIS product accuracy at the transit time (RMSE: 1.14 K to 5.53 K), LST data at the extended time all had higher accuracy (RMSE: 0.90 K to 3.57 K). (3)The method in this paper can generate more complete high resolution LST data set in space and time. On the one hand, it was less affected by high resolution images of missing values, based on the reconstruction of only low spatial resolution LST datasets, seamless and high spatial resolution LST datasets can be generated under clear sky and cloudy conditions. On the other hand, the effective time distribution of high spatial resolution images has little influence on the method in this paper, and the reconstruction results have high stability.In this paper, a land surface temperature downscaling method based on DTC model deviation coefficient calculation is proposed.The method was evaluated by observed datum of three stations and real remote sensing images. The results showed that the proposed method has higher accuracy and can obtain seamless high temporal and spatial LST data under clear sky and cloudy conditions. Moreover, the lack of high spatial resolution LST data and the effective time distribution have less impact on the proposed method. Because the proposed method in this paper is based on the DTC model, it is not applicable to the surface temperature downscaling under rainy weather conditions, which should should be investigated in future studies.关键词：downscaling of land surface temperature;high spatial and temporal resolution;space-time fusion;diurnal temperature cycle;FY-4A- 845
- |
- 240
- |
- 1
发布时间：2021-08-30 -
摘要：Due to the limitations of thermal infrared technology, a single sensor cannot provide both high frequency and fine spatial resolution Land Surface Temperature (LST) data. For solving this problem, it becomes an effective way by conducting the spatial downscaling of LST product with low-resolution and high frequency in collaboration with other auxiliary data. However, the existing LST downscaling methods do not fully consider the scale effects of different biophysical parameters on the distribution of LST, which makes the accuracy and spatial distribution of the downscaled LST are inconsistent. In view of this, taking Beijing and Zhangye as two study areas, this paper proposed a kind of LST downscaling algorithm to sharpen the MODIS LST using Multi-scale Geographically Weighted Regression (MGWR) according to the analyse of effects of NDVI, DEM, slope, latitude, and longitude on LST heterogeneity. Furthermore, four kinds of LST downscaling methods (i.e., TsHARP algorithm, ML algorithm, GWR algorithm, and RF algorithm) were introduced in this paper for further comparison and validation. Results show that the constructed LST conversion function based on the MGWR reveals the actual interaction between various scale factors and LST at various spatial scales. NDVI and slope have global impacts on the LST, while DEM and geolocation present local impacts on the LST. Compared with the four referenced methods, the downscaled 100 m resolution LST based on the MGWR has better spatial textures and displays clear landscape features in heterogeneous areas such as deserts and towns. In addition, all images predicted by the MGWR algorithm showed better accuracy, in which the area proportion under the 0—1 K error level were all more than 57%, the root-mean-square error (RMSE) were all less than 2.85 K, and the coefficient of determination (R2) were all more than 0.88.关键词：Land Surface Temperature (LST);spatial downscaling;MGWR;scale difference;MODIS- 1060
- |
- 431
- |
- 9
发布时间：2021-08-30 -
摘要：The downscaling method using spectral index as trend surface factor is widely used in remote sensing land surface temperature scale conversion. However, it is difficult to highlight the distribution of land surface temperature and describe the complex relationship between trend surface factor and land surface temperature in statistical model. Therefore, this paper constructs a Land Surface Temperature Downscaling Residual Network (LSTDRN) taking Landsat 8 ARD LST as downscaling objects and Landsat 8 OLI raw data as potential trend surface factors. The LSTDRN aims to explore the trend surface bands or combinations suitable for spatial downscaling of Landsat 8 land surface temperature products, and verify the spatiotemporal applicability of the model.In view of the strong nonlinear relationship fitting and feature extraction ability of deep learning, this paper proposes a LST downscaling model based on deep learning. In the training stage, the relationship model between the Landsat 8 ARD LST and all bands of Landsat 8 OLI (except band 9) is fitted at the low resolution level. The Huber loss function is used to minimize the residual between the prediction results and the label to realize the transformation residual constraint. Then the optimal model is obtained through iterative learning and parameter adjustment. In the test stage, the optimal model is applied at the high resolution level to obtain the final downscaling results according to the “scale invariant” hypothesis of land surface temperature downscaling. In addition to visual evaluation, the downscaling results and original LST data were scaled up to 100 m resolution for quantitative evaluation. After evaluating the downscaling effect of each band, the multi-trend surface factor downscaling experiment was carried out. Meanwhile, the deep learning method is compared with the classic traditional method TsHARP to compare the stability of different land surface types and different seasons.In LSTDRN, using the original single band of Landsat 8 OLI as the trend surface factor has a good downscaling effect, and increasing the number of potential trend surface factors can not improve the downscaling effect. In the experiments of different land cover types, the effect of deep learning method is better than that of traditional methods, and the best effect is when NIR band is the trend surface factor. The order of deep learning downscaling effect in quantitative evaluation of different land cover types is vegetation > building > water, however, there is no obvious difference between the traditional methods. In different season experiments, the downscaling effect in quantitative evaluation of deep learning method in spring, summer and winter is better than that of traditional classical methods, and the downscaling results of the two methods in autumn are similar. Therefore, the proposed LSTDRN has better downscaling effect on Landsat 8 remote sensing land surface temperature products, which is superior to the traditional classical methods and has stronger stability.The LSTDRN proposed in this study can make good use of Landsat 8 OLI single band data as trend surface factors for downscaling. With the increase of trend surface factors, the downscaling effect is not significantly improved. Compared with the classic traditional method TsHARP, the downscaling effect of deep learning method is less affected by land cover types and seasonal factors, and the difference of downscaling effect is rather small in different spatial-temporal conditions. The method has stronger stability, which is conducive to enhancing the applicability of downscaling research and promoting the application of high-quality land surface temperature data.关键词：remote sensing;land surface temperature;downscaling;Landsat 8;deep learning;trend surface- 1182
- |
- 370
- |
- 2
发布时间：2021-08-30 -
摘要：Land Surface Temperature (LST) is an important parameter in many research fields such as dynamic simulation of land surface processes, regional and global change analysis. It has always been a hot research topic on how to obtain land surface temperature with higher spatio-temporal resolution. Due to the limitation in the availability of satellite imagery data with high spatial and temporal resolution simultaneously, LST downscaling from coarse spatial resolution data is an effective method. Besides LST retrieved from microwave channels or thermal infrared bands, reanalysis dataset can provide long time series of hourly land surface temperature. If the reanalysis LST can be downscaled to produce reliable products with higher spatial resolution or not needs to be further studied.To compare downscaling results at various resolutions from raw reanalysis LST, two different regions in Zhangjiakou, Hebei province were selected as the test areas that represents the urban-rural and the mountainous characteristics respectively. LST at 100 m spatial resolution was retrieved by using Landsat 8 OLI/TIRS data through Mono-Window (MW) algorithm, which was then upscaled to different resolutions of 200 m, 500 m, 1000 m, 2000 m, 5000 m and 10000 m respectively. ERA5 LST data at the resolution of 10000 m is corrected based on Landsat 8 LST, which is downscaled to 6 resolutions by constructing and applying the random forest model. Elevation and six remotely sensed indices including NDVI, MASVI, MNDWI, NMDI, NDBI, NDBSI calculating from the corresponding OLI spectral reflectance were taken as the random forest model parameters. LST downscaling precisions at different spatial resolution within various landcover regions were then evaluated and discussed by using the derived Landsat 8 LST as the reference, and the feature importance of seven land surface parameters in random forest models changed with scales were also analyzed by comparing Gini index.The maximum, minimum and average values of the corrected ERA5 LST and the reference Landsat 8 LST are close at the resolution of 10000 m, but the standard deviation is lower than that of reference LST. The downscaling results of different resolutions can accurately express the LST spatial distribution characteristics for the two experiment areas. As the spatial resolution changes from 5000 m to 100 m, the downscaled LST texture accuracy is significantly improved, however Root Mean Square Error (RMSE) gradually increases and the correlation between the downscaled and the reference LST decreases. RMSE grows from 1.16 to 1.79 ℃ and from 1.61 to 2.49 ℃ for the urban-rural area and the mountains area respectively. For the random forest downscaling model, features importance have no significant change at different resolutions, which to some degree indicates that the random forest model has a relatively stable scale invariant characteristic. NDVI shows higher importance affecting LST distribution in two test areas, and elevation is the most important parameter in mountainous area.The research results show that ERA5 LST and Landsat 8 LST have good consistency in the spatial distribution, which means ERA5 LST has the potentiality to be downscaled to express detailed land surface temperature at higher resolutions. While downscaling ERA5 LST to higher spatial resolution, larger underestimation and overestimation errors will occur in the high temperature area and low temperature area respectively.关键词：ERA5 reanalysis datasets;land surface temperature;Downscale;random forest;spatial scale- 919
- |
- 244
- |
- 0
发布时间：2021-08-30 -
摘要：Surface temperature is an important variable that determines surface radiation energy budget, and plays an important role in the study of energy balance and water balance in the lithosphere, hydrosphere, biosphere and atmosphere. Thermal infrared remote sensing technology can be used to achieve rapid acquisition of regional and global land surface temperature, which has been widely concerned by researchers. At present, FY-3D is the earth observation satellite with the highest spectral resolution in China, which has greatly improved the earth observation capability. The Moderate Resolution Spectral Imager (MERSI-II) carried by FY-3D has been greatly upgraded and improved, and its performance has been significantly improved. The spatial resolution of thermal infrared data has reached 250 m. In this paper, the atmospheric radiation transfer model MODTRAN 5 is used to simulate the MERSI-II thermal infrared channel satellite observations. On this basis, construct the land surface temperature inversion model of the generalized split-window. Combined with ASTER GED global surface emissivity product and MERSI-II atmospheric water vapor content inversion algorithm, the technical process system of remote sensing inversion of land surface temperature is developed. Finally, the proposed method was verified by using the measured surface temperature data from several stations in the Wuhai of Inner Mongolia and SURFRAD stations in the United States in August 2019. The results show that compared with the measured surface temperature data, the RMSE of the land surface temperature inversion based on the generalized split-window algorithm is between 1.6 K and 2.6 K for all sites. The accuracy of inversion has reached the expected target, and it has a high spatial resolution, which can be used in the inversion of operational surface temperature. At the same time, it also shows that the accuracy of radiometric calibration is guaranteed to a certain extent, which effectively meets the application requirements of regional and global surface temperature remote sensing monitoring.关键词：land surface temperature;split-window algorithm;FY-3D;MERSI-II;MODTRAN- 1052
- |
- 571
- |
- 2
发布时间：2021-08-30 -
摘要：Land Surface Temperature (LST) is one of important variables for urban thermal environment studies. Urban surface is extremely complex and LST is heterogenous. High spatial resolution of LST is helpful for fine urban thermal environment monitoring and mitigation. However, so far, as we know, mostly LST downscaling studies focus on two-dimensional scope, and lack of building three-dimensional (3D) structure impact on LST. This study will use random forest model (RF) with both 2D and 3D land surface indices for downscaling of MODISF 1 km LST to 100m. Meanwhile, the spatial scale issues of building 3D morphology on LST is also discussed. In addition, in order to make up for the lack of theoretical basis of RF model, this study added more parameters during RF downscaling model generation based on the thermal radiation transmission equation, e.g. land surface radiance (MOD02) and precipitable water vapor (PWV, MOD05).The results show: (1) When MOD02 and MOD05 are included in RF model, RMSE and R2 between simulated 1 km LST and MODIS LST product are improved from 3.1 K and 0.5 to 0.38 K and 0.94. (2) When building 3D morphology is included in RF model, the OOB_score improves from 0.46 to 0.49. The R2 between simulated 100m LST and ASTER LST product is slightly decreased, one of the reasons is that LST retrieval methods of MODIS and ASTER are different and the two sensors are also different. However, when MOD02 and MOD05 are included, RMSE and R2 improve from 2.4 K and 0.29 to 1.2 K and 0.68. (3) The OOB_scores with building morphologies improve at both 1 km and 100 m scale, and the importance of building morphologies are different. Above all, downscaling MODIS LST in urban area should consider land surface radiance, PWV and building 3D structure indices, and impact of building morphologies on LST are different at different spatial scale.关键词：remote sensing;land surface temperature;downscaling;random forest model;building morphology;spatial scale effect- 1032
- |
- 363
- |
- 1
发布时间：2021-08-30 -
摘要：The study of radiometric correction physical model for geometric effects applicable to thermal infrared remote sensing images in urban areas plays an important role in improving the inversion accuracy of underlying surface temperature. In this study, the sky view factor (V) was used to quantify the geometric feature of urban underlying surface, considering the influence mechanism of the geometric effect on radiation transfer of the urban ground target. The radiance observed by the sensor was decomposed into three parts: radiance emitted by ground target, radiance reflected by ground target and upward radiance of the atmosphere, and the radiation transfer process of each part at the “urban -atmosphere” interface was analyzed. Then we constructed the Urban Thermal Radiation Transfer (UTRT) model for the urban underlying surface, which can be applied to the geometric effect radiation correction of thermal infrared remote sensing images in urban areas. Based on Landsat 8 TIRS(Thermal Infrared Sensor) data, the UTRT model was used to inverse surface temperature in a typical urban area of Beijing and compared inversion results to the model without considering geometric feature of the underlying surface. The results demonstrate that: (1)The inversion result of the UTRT model is generally lower than that without considering geometric feature of the underlying surface with the difference range 0—1.57 K and average value 0.44 K. (2) Through the analysis and comparison of different land cover types, the results show that buildings and bare land types have a more significant difference, and their spatial distribution is obviously affected by the V value. The V value is proportional to the inversion result within a specific range. (3) Sensitivity analysis of the sky view factor and ambient temperature parameters were conducted for the UTRT model. The sensitivity of the V parameter is stronger in scenes with small surface emissivity, while the sensitivity is lower due to the ambient temperature being closer to the target feature temperature. The study shows that the UTRT model can provide a solution for the radiation correction of the geometric effects of the remote sensing image in urban underlying surface. Compared with the model that does not consider the geometric feature of the underlying surface, the former can be better applied to the remote sensing inversion of urban surface temperature. This study has development potential and application prospects for advancing the quantitative remote sensing research of urban high-resolution thermal infrared.关键词：thermal radiation transfer model;urban underlying surface;geometric feature;sky view factor;land surface temperature- 2040
- |
- 267
- |
- 0
发布时间：2021-08-30
Technologies and Methodologies
-
摘要：Atmospheric water vapour, an important component of global greenhouse gases, is one of the key factors influencing global climate change, hydrologic cycle, matter-energy exchange and so on. The total water vapour content in the atmosphere is usually quantified with the parameter of Total Precipitable Water (TPW). As the first launched satellite of Chinese Fengyun-4 series, FY-4A satellite has been equipped with the Advanced Geosynchronous Radiation Imager (AGRI). This paper aims to retrieve the TPW under the clear-sky atmosphere with the combination of the middle- and thermal-infrared remote sensing observations of AGRI, ERA5 total column water vapour reanalysis product and Integrated Global Radiosonde Archive (IGRA).On the ocean surface, the regression analysis method and random forest algorithm have been introduced to retrieve the TPW successively. In random forest modeling, the standard deviation of every regression tree’s output value is used as quality control criterion, and the retrieval results are considered as reliable only when the standard deviation is less than 0.4 cm.On the land surface, the Split-Window Covariance-Variance Ratio (SWCVR) method and random forest algorithm have been developed to retrieve the TPW, and the elevation and Normalized Difference Vegetation Index (NDVI) estimated at the top of atmosphere have been added to the random forest model in order to reflect the information of land surface.In terms of TPW retrieval over ocean, the determination coefficient is 0.817, the Root Mean Square Error (RMSE) is 0.493 cm, and the Mean Absolute Error (MAE) of the retrieval results is 0.393 cm compared with the ERA5 total column water vapour product when using the regression analysis method. For the random forest model, the quality control passing rate of retrieval results accounts for 84.0%, the determination coefficient is 0.956, RMSE is 0.247 cm, and MAE is 0.183 cm when compared with the ERA5 total column water vapour product.In terms of TPW retrieval over land, for the reason that the spatial resolution of AGRI is only 4 km, the SWCVR method is unable to retrieve the TPW accurately using the observations of AGRI. For the random forest model, the passing rate of retrieval results accounts for 93.2%, the determination coefficient is 0.972, RMSE is 0.155 cm, and MAE is 0.106 cm when compared with the ERA5 total column water vapour product. In the end, the radio-sounding water vapour is introduced to evaluate the accuracy of the random forest model, and the passing rate of retrieval results accounts for 75.6%, the determination coefficient is 0.946, RMSE is 0.215 cm, and MAE is 0.163 cm.First of all, TPW over ocean can be retrieved with the middle- and thermal-infrared remote sensing observations of AGRI by using the regression analysis method and random forest algorithm, and the retrieval accuracy of random forest model is obviously higher. Besides, the SWCVR method is unable to retrieve the TPW over land accurately due to the low spatial resolution of AGRI, meanwhile the random forest algorithm performs well. The results indicate that the random forest algorithm can effectively improve the TPW retrieval accuracy with thermal infrared remote sensing observations.关键词：FY-4A AGRI;thermal infrared remote sensing;Total Precipitable Water (TPW);Regression analysis;Random Forest- 956
- |
- 310
- |
- 2
发布时间：2021-08-30 -
摘要：Land Surface Temperature (LST) and Air Temperature (AT) are important parameters of land-atmosphere interaction system. In the process of land-atmosphere interaction, Water Vapor Content (WVC), NDVI index, carbon dioxide concentration and other factors will have great impact on land-atmosphere heat transfer. Therefore, we investigate the spatiotemporal variation trend of annual maximum of LST(AMLST) and annual maximum of AT(AMAT) from different data source including remote sensing and meteorological station in China in recent 20 years.The trend analysis method is used to analyze the spatiotemporal variation trend of AMLST and AMAT from 2003 to 2018. Combined with the annual maximum of WVC(AMWVC) and NDVI index, the driving factors leading to the variation of AMLST and AMAT were analyzed.The results showed that: (1) The spatial distribution characteristics of AMLST in China from 2003 to 2018 is higher in the north and lower in the south, and the highest AMLST appears in Turpan Basin, Xinjiang. The spatial distribution of AMAT in China is higher in the south and lower in the north. In virtue of interaction between AMWVC and AMAT, the two impact factors appear a certain coincident characteristics. (2) During the period of 2003-2018, the spatial distribution of climate inclination rate of AMLST is higher in the north and lower in the south, the AMAT climate inclination rate have the same characteristics with AMLST. From the perspective of variation range, the AMLST is larger than that of AMAT. But it is inconsonant in Tianshan Mountains, Sanjiang Plain and the south side of Qinling Mountains. (3) The increase in AMWVC makes the AMAT rise, while the increase of vegetation coverage can decrease the AMLST. In the Tianshan area, the response of AMLST-AMAT interaction system to the AMWVC is not obvious, but the relationship between AMAT and the growth rate of carbon dioxide concentration is obvious. (4) The spatial distribution of the AMLST retrieved from remote sensing data is different from that observed by stations, but the temporal variation trend is basically the same.关键词：annual maximum surface temperature;annual maximum air surface temperature;water vapor content;NDVI index;climate tendency rate- 822
- |
- 210
- |
- 3
发布时间：2021-08-30 -
摘要：In recent years, as China’s afforestation plan continues, vegetation cover has continued to increase, which in turn affects China’s surface environment. Land Surface Temperature (LST) is an important parameter that characterizes the physical processes of the surface, and is the driving factor for the energy exchange between the surface and the atmosphere. It is widely used in the research of basic subjects such as climate, hydrology, ecology and meteorology, and is one of the key parameters in many basic researches. To study the impact of afforestation strategies on local and regional scales and guide the implementation of corresponding policies, this study uses the IBM (Intrinsic Biophysical Mechanism) method to investigate the impact of afforestation in China on LST, and discusses the effects of radiative and non-radiative forcing and different vegetation cover types on LST. The results show that: (1) the influence of afforestation on LST is shown as a warming effect in the cold season in high latitudes, but as a cooling effect at all latitudes in the warm season. (2) The radiative effect of afforestation on LST in high latitude areas in cold season is relatively strong, while in other seasons, the non-radiative effect of afforestation on LST at various latitudes is dominant, and the radiative effect is relatively weak. (3) When open land is converted to forest land, different types of forest land cover have different characteristics of impact on LST. When open land is converted to deciduous broad-leaved forest, the impact on LST is similar to the overall impact of afforestation on LST, showing a warming effect in high latitude areas in the cold season, and a cooling effect in low latitude areas. The effect of open land into evergreen coniferous forests and evergreen broad-leaved forests on LST is shown as a cooling effect. This study analyzes the impact of afforestation on LST in the context of continuous afforestation in China. The research has practical significance for current afforestation policies and provides theoretical guidance for future afforestation strategies.关键词：land surface temperature;IBM method;afforestation;non-radiative effect- 1217
- |
- 307
- |
- 2
发布时间：2021-08-30 -
摘要：All-weather LST with higher spatial resolution are important for the detailed monitoring and early-stage warning of glacial debris flow disasters in Southeast Tibet. Based on the latest 1-km all-weather LST (TRIMS LST) product, a variety of LST downscaling methods were compared in this paper, and the optimal downscaling method was determined to generate all-weather LST at 250 m in the glacier regions in Southeast Tibet.The application of remotely sensed all-weather LST with insufficient spatial resolution in fine disaster monitoring has many limitations. At present, there are few studies on the spatial downscaling of all-weather LST. In this paper, the 1-km/250-m elevation, slope, aspect, land cover type, vegetation index, surface reflectivity, and snow index were used as the descriptors of LST. Combining the moving window strategy, a variety of LST downscaling methods were compared in this paper, and the optimal downscaling method was used to improve the spatial resolution of all-weather LST (TRIMS LST) from 1 km to 250 m.Based on the RMSE between the 250-m all-weather LST generated in different moving windows and the original 1-km TRIMS LST, and the 20×20 km is determined as the best downscaling window. The evaluation results based on the measurement data from ground sites and image quality indices show that LightGBM has optimal downscaling performance. The RMSE and MBE between the 250-m all-weather LST generated by the LightGBM and the measurement soil temperature during the day are 2.25 K and -0.01 K and during the night are 2.15 K and 0.74 K. Compared with the original 1-km TRIMS LST, RMSE and MBE were reduced by approximately 0.25 K. Evaluation results based on the air temperature from ground sites show that the 250-m all-weather LST also has good reliability at the glacier. The Q index shows that the 250-m all-weather LST generated by the LightGBM not only maintains high consistency with the original 1-km all-weather LST in terms of spatial pattern and amplitude, but also provides a large amount of spatial detailed information of surface temperature. The SIFI index further indicates that the 250-m LST has high image quality without over-sharpening.The all-weather LST with a 250-m spatial resolution can be used as reliable and high-resolution LST data for the monitoring and early warning of disasters such as glacial debris flow in the glacier area of Southeast Tibet, which have positive significance for disaster monitoring in this area. Since the spatial resolution of some descriptors (e.g., vegetation index) is 250 m, the all-weather LST can only be downscaled to a spatial resolution of 250 m in the current stage. How to improve the spatial resolution of descriptors for further determining a higher spatial-resolution all-weather LST still needs more in-depth research.关键词：LST;downscaling;all-weather;glaciers;Southeast Tibet- 866
- |
- 423
- |
- 0
发布时间：2021-08-30
Remote Sensing Applications
- Postal code：100190
- Tel：010-58887052 Email：nrsb@aircas.ac.cn
- Technical support is provided by Beijing Founder electronics co., LTD 京ICP备20021838号-9
京公网安备11010802024621 - It is recommended to read the content of this site in Chrome&IE9+. Please switch to extreme mode in browser 360.
- Cookies We use cookies to help provide and enhance our service and tailor content. By continuing, you agree to the use of cookies.
0